So we're gonna go ahead and get started So welcome to building a robust cloud foundry. My name is Duncan win I work for Pivotal and I work for a cloud foundry services team and my name is Hayden Ryan and I also work on the same team as Duncan essentially what our team does is we spend about half of our time with the engineers Working with them and then the other half of the time working with customers Taking them say from a POC and building them straight through to MVP one Now I must apologize if I do cough during this presentation. I'm still really quite sick with the flu So just bear with me. I apologize Yeah So in this talk, we're gonna focus on our experience making cloud foundry a bus So really hardening cloud foundry and that's looking at taking the four levels of HA for the forward to make cloud foundry highly available Looking at how you recover from a disaster situation. So if the worst happens, how do you bring back cloud foundry and boy and bush? Excuse me and also security. So how do you lock cloud foundry down to make it more secure for your end users? So we're gonna jump straight into high availability HA So when most developers think about high availability, they're really thinking about their apps and their services How you keep those services running in a performance reliable recoverable way So they're a joy to use for the end user. They don't go down. They just work And if something does go wrong, there's timely error detection But when you look at cloud foundry cloud foundry does this for you especially around your applications It's got four levels of HA built-in already. So if you start with the application instances Between the health manager and the cloud controller cloud foundry will look at the actual state versus the desired state And if an application instance goes away or bring it back for you If a platform process dies the platform processes are being monitored by a monet and Monet will restart or try and restart that process for you If something more catastrophic goes wrong if a VM goes away Then one of the many actions that Bosch can take is to actually resurrect that VM and bring that VM back for you And finally, there's this concept of availability zones where you can stripe your DEA is across different availability zones So if you lose an AZ or if you lose a disk store, then you still have your applications running So what's left for us to do? Well, really we're focusing on this concept of availability zones and extending it to beyond just DEA's To the actual cloud foundry components themselves so that we can keep cloud foundry running in a performance highly available way So for companies who really need this and this means that their needs are beyond just a dr scenario They need to recover quicker than they can from a disaster scenario We see two patterns that have come up again and again The one on your left here is two cloud foundry deployments in two different data centers This could be one cloud foundry on Amazon and one on vCenter It could be active passive or active active active and you can then load balance between these two environments The one on the right is to take a single cloud foundry deployment and then split it across two different AZs So we're going to look at the trade-offs between the two here We're going to start with the one on the left This does mean that you have to take your application and deploy it into two different locations So simple to deploy you're just deploying cloud foundry twice But the operation of that is more complex and you can deal with some of that complexity through a CI pipeline So how does it work in terms of traffic flow the end user they're agnostic to the underlying environment They don't care which data center they hit they just target my app at my cloud foundry.com And they then come into a GTM a global traffic manager Typically for most companies we worked with that's like an F5 load balancer that consults a DNS resolution service So through smarts like geo based IP routing and looking at various different load It works at which data center and which cloud foundry to route the traffic onto So it comes into a specific data center and hits an LTM a local traffic manager again for most companies That's something like an F5 load balancer on appliance That should have a VIP virtual IP and so if that appliance fails There's another appliance on standby to grab that VIP and then take over so it's a very fast failover now in this scenario we had a problem and that the appliance the physical appliance sat on the corporate network and It dealt with traffic from a number of other departments And they didn't want traffic to be decrypted or you know SSL terminated at that layer and then pass unencrypted traffic to the sensex boundary They were using this NSX boundary for their software to find networking and they were using subnets behind the seams So what they did is they re-encrypted the traffic passed it behind the NSX effectively firewall And then they decrypted an HA proxy layer So it meant we had three layers of load balancers, which isn't great But doesn't really matter too much and you could easily take out the third load balancer by putting a physical appliance in that NSX layer One other point to note is that we had the certs for both data centers at the LTM layer And this is because if something went wrong if you couldn't hit that cloud foundry instance Then you could still route traffic over to the other data center a couple of other considerations We always advocate to have two different domains one for system and one for applications because you don't want your developers To start registering apps like UAA and cloud controller. You want to keep those separation of concerns Because we had two cloud foundry installations We did want to give the developers the ability to target a specific cloud foundry foundation And so we ended up with four domains two system and two application one for each data center But as I mentioned the end user they need to be agnostic so you end up with this generic my app and my cloud foundry.com The last key consideration with this is services and this bites a lot of people How do you keep your data concurrent across two data centers? And ideally you need something like a stretch layer to network with minimal latency something like sub five milliseconds The last customer we worked with We looked to analyze their data usage and they were using an awkward database But they had an application with a long running session and they just needed to cache data for that session It's like a 45 minute session for a call center app so we looked at using Redis as a Cache and it was really appropriate But this solution with one app targeting two Redis clusters to keep the data concurrent is really ugly for the developer So next we explored instead of putting the onus on the developer to keep data concurrent in two data centers Why not have a service and have the service manage that concurrency and write to two data sources? It's better because the developer doesn't need to deal with that complexity But you still have the latency going across two data centers, and it's still not great So my preferred solution is to use some technology built for this something like Gemfire or Cassandra which can propagate data synchronously or asynchronous synchronously over a WAN and they have Collision detection algorithms and all of that good stuff So they're really designed to keep data concurrent and consistent between two data centers So the second deployment that we're going to look at is a single foundation of cloud foundry split across two availability zones Now as Duncan has already mentioned Quite a lot of the time what we end up deploying really depends on the customer So companies have very unique environments. They have very unique requirements and One of the fantastic things about cloud foundry is it's very flexible in the way that you can deploy it So in my case study We have a few of the examples of unique requirements that companies may have With the particular customer I have in mind We had multiple multiple deployments of cloud foundry in a single VPC We had very restricted IP ranges So we had to override all the subnets or the IPs the site is etc. And we had some Routing requirements as well. So we had to use their internal corporate DNS We weren't allowed to use route 53. So this was on Amazon and We also weren't allowed to use elastic load balances So what this ended up meaning is that the way that we deployed cloud foundry was a little bit different to the way that we would Normally recommend deploying cloud foundry in an AWS VPC So this is what we ended up deploying there Their DNS was a bind DNS. So it was actually in an external data center. So it was not actually in Amazon itself We use direct connect to get into the VPC where the customer had a customer managed bastion box They also had a customer managed NAT box. So that kind of took that out from us managing things like security etc around that and then We have our two Oops, where's it gone? There we go our two availability zones that we split cloud foundry over Now I'm not going to go into too much of the detail with all the jobs in this particular slide But hopefully if you can't see the slide that well, you can download the slides at the end So this is how like let's abstract it up a level This is what we ended up doing. So using SSL termination at the HA proxy level using bind DNS round robin now that was Imposed on us from the customer this meant that if an availability zone did go down There'd have to be a manual step to actually D-register the IP for the HA proxy or the HA proxies for that availability zone So that did add kind of almost a single point of failure But um that was mitigated by a failure matrix, which we provided to the customer Now the reason that we have two HA proxies in each availability zone and to go around as backing them Was so that if an availability zone did go down The instances that we already had deployed would be performant enough to handle the increased traffic load Similarly, we also scaled the DEA's So let's start looking a little bit further into this particular customer's deployment of cloud foundry So what we started to look at was who does cloud foundry need to be highly available for there's really three classes of users So there's your end users that are consuming your applications There are your developers who are developing applications reading logs and pushing them and There are your operators as well so your operators are operating the cloud foundry environment and Maintaining it keeping it highly available providing upgrades, etc So with this particular customer their main priority that required a hundred percent uptime was their end users so straight away that makes all of the Cloud foundry components that are related to getting applications running or keeping them running and providing data through extremely critical So we needed to make sure that all of these were deployed in a highly available manner with at least one instance in each availability zone Some of the other Jobs we defined as not so critical. I mean still it very important obviously But if there was a little bit of downtime with these jobs The effects would not necessarily take down cloud foundry or the or the user's apps So these were things like logging The health monitor if the health monitor goes away. It just means it's not monitoring your apps So if there's a short period of time where it's not monitoring that might be okay depending How many levels of HA you've still got running? As well as things like Bosch, so again Bosch if Bosch goes away That means you don't have a resurrector you can't resurrect VMs. It also means that you can't administer the cloud foundry environment But you know is that critical to the operation of cloud foundry? It really depends on your use cases so in this case it just kind of Removes one to two levels of the high availability that Duncan spoke about earlier and there were some things that were not so critical so The clock global is essentially a cleanup job. So it tells the cloud controller Periodically to clean itself up. This was not critical. So we decided that That could only have one instance in one availability zone. We didn't need to replicate it Similarly having a jump box. We did have a bastion box as well that was Set by the customer. So we decided that spinning up a jump box wouldn't actually take that long In a downtime situation, it would just increase the time to recovery so we ended up deploying minimum of one instance in each availability zone for all of these particular jobs for the customer so Choosing a deployment topology really comes down to a lot of different factors So there's no one-size-fits-all. You need to be very cognizant of the trade-offs between each of these apologies For a lot of customers and companies a single deployment in a single data center That's highly available within that data center and a really good disaster recovery Procedure is enough to push your company forward Other companies will require Levels of ha so you could either use a dual deployment So two single deployments across Different I as is you could have them in different regions The issue with that is it's quite easy to deploy, but it's quite hard to administer and develop for It's also quite hard to push apps to both of them But that can be mitigated by using a CI CD pipeline and the final deployment is Quite complex because you have to be very cognizant of all the individual components of cloud foundry But once it's deployed, it's quite straightforward. It's just a single deployment. So it's easier to deal with Okay on to a disaster recovery So how should you back up Bosch and how should you back up cloud foundry and it really comes down to these core components You need to back up your NFS server or your blob store so that your compiled packages and your artifacts are backed up You need to make sure you back up your configuration and preferably source control that as well So your Bosch manifests and any vendor configuration as well So for example in our case pivotal cloud foundry back up ops manager and you also need to back up your cloud foundry databases and your Bosch databases When you look at this, this is effectively your cloud foundry in its rawest form Everything else around that it's just wiring. It's just processes and you can bring all that stuff back But this is the stuff you need to stay around So we're going to explore a couple of scenarios here And I'm going to take you through how we do it with pivotal cloud foundry and especially with ops manager and also How you would do this with just open source cloud foundry and Bosch So the first scenario is what happens if you lose ops manager? We use ops manager to deploy cloud foundry So for us ops manager is really critical and what happens if you lose your cloud foundry deployment? How do you get that stuff back? For ops manager, it's really simple you can export this configuration and Then if everything goes up in flames You bring back a new ops manager and Then you can import that configuration and providing you're Using something like an external database an external blob store for cloud foundry. Yeah, everything's fine To export ops manager you go into the UI and just download the installation settings It's really straightforward if you're really paranoid You can also copy the deployment manifests and with these deployment manifests with Bosch You can actually bring back your deployment if your deployment goes away But ops manager will do that for you as well if you're not using ops manager Then you do just that you make sure you have a copy of those deployment manifests So you can download those from Bosch and if your deployment goes away Then you can use those manifests to bring back your deployment and everything's good So Bosch is critical for bringing back your deployments and the Bosch director is critical for that So what happens if you lose that Bosch director if someone goes and deletes that VM if it's a single VM What we call micro Bosch or something else happens with it? How do you recover from that scenario? So we start with the same approach you back up your configuration and when you deploy the Bosch manifest Bosch director you have this manifest Bosch YAML and Ideally you need that manifest to bring back Bosch But herein lies a perceived problem in that when we go to the directory where that manifest should exist It's been deleted and the ops manager does this with good reason that manifest contains your AWS Secret keys and other sensitive information so we don't want to leave that manifest lying around in plain text on the file system So it deletes it So you don't have it and when everything else goes up in flames. How do you recover? So you bring back ops manager in the same way forward In the same way as our previously and then ops manager and this is a secret source It's got some capability to reconstruct that Bosch director with the same IP same set of credentials And so ops manager will actually bring that Bosch director back for you So how do you do this if you're not using pivotal cloud foundry an ops manager? So I would argue you absolutely need to keep that Bosch manifest around it's got all your credentials It's got all the information about your Bosch director And so ideally you need to source control that and you need to keep it And then with that YAML file with that manifest you can bring back your Bosch director But you need more than that because Bosch needs to be aware of all those deployments which you've already got out there So you have you have a couple of options here If you go into Bosch and you use monitor stop to stop all the processes You can back up the Bosch database if you're using the internal database And then when you bring back a new Bosch, you can do the same and then you can import that data into your new database If you need some more information and you're using the internal file system I'm sorry the NFS store then you can snapshot the disk Again go into Bosch stop all the processes detach the new disk and reattach the snapshot My preferred solution is to use an external Bosch database and an external blob store And then when you bring back Bosch on the same IP you just connect it up to your data store Store your database in your blob store and away you go When you're backing up the cloud found your databases You need the DB encryption key for the cloud controller because it's encrypted So in order to back up that and restore that database you need that encryption key In addition with the blob store you need to think about how you back that up as well So in this case using s3 we set a policy to deny bucket deletion And that meant that that bucket is never going to go away. The girl is not going to change So you've always got that bucket and then you can turn on versioning for that bucket or for the contents of that bucket And if someone goes in and maliciously or accidentally deletes that content you can restore that content in your bucket store there so wrapping up Quite a lot of the time Companies think of security and they think of it as an afterthought Security should not be an afterthought. It's actually really important So what I want to do is provide you a little bit of a primer some basic things that we've seen out with customers That they've done, you know potentially good things and just share them so that you can all kind of get a start in in cloud foundry security So security is a hard problem It's not a problem that is solved once it is a process that needs to be continually solved to continually updated continually managed as well so iterating through Security and security issues is really important Similarly feedback from any issues that have arisen say security incident reports Obtaining management support to hold meetings to discuss security, etc. Is fundamental so you need that Organizational backing before you can even start with security really So in security there are three main concepts The concept of restriction so using Access authentication and authorization to restrict access to your VMs and your jobs Limiting so limiting the scope if there has been a compromisation of a VM or a job as Well as mitigating any security breaches that do or potentially occur So let's let's dig down into them The first step is to restrict users so this is mostly talking about restricting users accessing your IS level your cloud foundry installation As well as things like Bosch So the number one step that we recommend is use multi-factor authentication wherever possible So each user should have an individual account something that can be attributed to them something that can be put into an audit trail To potentially find any issues that have occurred or any unwanted changes and Also lock that user out if their details have become compromised or if they're no longer with the company or disgruntled or whatever So multi-factor authentication at a minimum on the IS level Similarly, you can actually put MFA on to jump boxes. So when you log in using SSH Using an RSA key to actually provide a token It takes it from just something they know to something that they have as Well as something that they know in addition to that we also recommend that all Bosch users have their own separate accounts again this creates the ability to Individually identify and target any compromised user names and passwords that come up and finally github So you'll hear me kind of rabbit on a lot about audit trails, etc When you check your manifest into github, it's really important that you don't just use one Username or say a group username that each person has their own individual accounts and they're very disciplined at actually sending that information to github one of the issues that we found on one customer site was that they used a jump box with a single username and password and because of that whoever logged into github Was getting all of the all the credits for all the changes that would be made So unfortunately that was not good in terms of having an audit trail The second step that you need to do is to restrict packets So this is best done at the IS level or is done at the IS level I should say So I've focused mostly on Amazon security and Amazon deployments So a lot of my slides are going to be a little bit more targeted to that so on Amazon We have security groups these are Security policies defined at the instance level so they can span subnets They can span availability zones, etc and define ingress and egress rules Similarly you can use access control lists Which are at the subnet level we find that most customers don't actually use these but the option is there and Finally routes restrict where data can go so data from an ELB May come in and it will only be able to go to say HAProxies or to the routers Cloud Foundry itself has security built into it. You can set network properties to allow and deny IP ranges and ciders It is important to note though that the application security groups actually override this so if you're Creating a new Cloud Foundry deployment. You're best off locking out using the design networks basically everything except for of course the VMs for Cloud Foundry and Then progressively allowing Access using security groups at the application level through the CFC li Sorry Okay, so what I wanted to do is basically diagram out a The security diagram of pivotal Cloud Foundry 1.4 It just is a bit of a this is how our engineers have said that we should do it At a basic level So you'll notice straight away that We have a single VPC and this is all in one availability zone by the way We have a public and a private section So we've got a demilitarized zone which is accessible to the internet and then we have our private subnet Which is running all of our instances and all of our databases now One of the fundamental things when setting up security rules is to have separation of concerns So you'll notice that all of the VMs essentially or The instance types have separate security groups. So these red lines around them So ops manager is a separate concern. So it's got a separate security group The NAT box has its own security group as does the ELB and The Cloud Foundry VMs in the elastic runtime In terms of what you actually allow to come into this environment It is quite a good practice to restrict down access to the ops manager So by default have this security group to not allow any ingress whatsoever and force your users to actually go to AWS and enable access for themselves This also allows an audit trail using things like cloud trail, which we'll talk about a little bit later Now by default you'll notice that all of these security groups here are allowing ingress from anything in the VPC This is a default state. You can actually lock it down further For instance a couple of things you might want to look at doing is having the elastic runtime only allow Traffic from the ELB security group on say four four three or port 80 Similarly only allowing traffic from the ops manager security group on the Bosch CLI ports Which there's a quite a few of them if an attack has been successful The next thing that you need to focus on is limiting scope if there has been a compromise So let's say that run has become compromised An attacker has got a script on there We want to limit the ability for them to then jump on to other cloud foundry VMs and Access the data behind them one of the most basic things that we see the customers don't always want to do is having Different usernames and passwords for the different jobs on cloud foundry We recommend that both the usernames and the passwords be a random string of characters say about 20 characters long With the it uses both cases and that you avoid using YAML characters So cloud foundry is deployed using YAML You don't want it to get confused or you don't want Bosch to get confused if you're using special characters that it interprets differently So I personally prefer not to use any special characters whatsoever and just have like uppercase and lowercase So what does that really mean avoid using cloud cal for everything? Let's say there has been a security breach. It is important to Understand what has happened and how it has happened. So standard security post breach rules apply You should isolate any VMs that have become infected or compromised In AWS the way that you do that is you attach a special security group to that VM That only allows ingress from you and allows no egress out of that security group the cool thing about Bosch is that Bosch won't be able to see that VM anymore. So it'll resurrect a new one for you pretty cool But the most important part is it won't be compromised because it's resurrecting it from scratch Then you need to investigate how the that particular box was compromised. I Would recommend rolling basically everything in your deployment for new passwords usernames, PEMs and most importantly your IaaS credentials and You don't want the the attack have been able to spin up new boxes in your AWS installation and The final one is to have a good feedback loop have incident reports, etc. Management visibility to provide better and Consistently improving security. We gave this talk internally and one of the questions that came up Was how do you deal with the operator who gets very disgruntled and then Wants to take your environment down completely as they leave so One of the best things that you can do is really restrict individual IaaS users ability to Delete so we want to avoid them deleting S3 buckets We don't want them to delete subnets or VPCs. This is because the subnet ID is actually used in the manifest So depending on how you've backed it up It could be problematic bringing it back and the final thing of course you don't want them deleting your backups Everything else can be recovered. So that's the important point to take the other thing is AWS provides some support for advanced monitoring of users it provides through cloud trail things like cloud trail alerts so that If a particular say security group has had things changed in it, you can actually alert a wider group So this becomes really really important really useful because you can build audit logs. You can build safety Into it and you can also have the ability to roll back changes that have been made So we're at the top of our session So just do a couple of quick takeaways When you're looking at deploying an architect in cloud foundry especially in a high available scenario You really need to understand the trade-offs and the environmental constraints. There's no one size fits all It comes down to what you're actually trying to achieve and what level of ha and what level of dr you need Specifically at the service layer You need to be cognizant of the impact of dual data centers and any impact It might have what type of data you have how you want to persist it how you want to replicate it How you keep it concurrent all of those concerns You have to be aware of any corporate security concerns networking constraints Because they can also shape and affect how you deploy and tune cloud foundry When you look at backup, you need to back up your configuration You need to back up your databases and your blob store because this is your cloud foundry and Finally, you need to think about the usage of cloud foundry How things get used the egress and ingress of traffic and locking cloud foundry down to make it as secure as possible And with that, thank you very much. Thank you


There's a part in the Course where Jesus says you are not asked to see your brother without the body. The real question you should ask yourself is do I want to see him sinless? So I think that's probably one of the greatest metaphysical boasting tactics of practicing the Course. When Jesus came under 2,000 years ago, he never taught publicly that the world was an illusion. He demonstrated by his attitude that it was, but he never spoke those words. So now it's an interesting phenomenon 2,000 years later to have a book where people kind of thumb through it and they go, oh, this is what it's teaching. Well, actually it's aimed at teaching forgiveness and it offers a training program to come into that forgiveness, but nowhere in the book does Jesus ever give the instructions to go around and tell people and treat people as if they're illusions. Talk about going from A to Z and going around and dinging everybody with Z when clearly there has to be an emotional catharsis, a transformation of consciousness that goes first in which you come to a state of mind, but that's the highest state of mind.  It's very attached to bodies, it's very attracted and repulsed by many things about bodies and he's got a whole training program, but he's not really saying that you're to go around and see your brother as an illusion, he's just saying, are you willing to see him sinless? And part of that is to let those judgments and those emotions come up into awareness so that they can be dealt with, not to try to skip over them. He actually goes so far one time to say that really that would be like the inappropriate use of denial. The appropriate use of denial is to deny the belief that error can hurt you and to try to dismiss or disregard the body completely is the inappropriate use of denial in the sense that the body is part of your awareness as you perceive yourself. So if you deny part of the awareness as you perceive yourself, you are literally denying the power of the mind because the power of the mind was used to project the body. And you see if you try to skip over to Z and just go, you're an illusion, it's an illusion. It's an illusion. It's probably the biggest example of metaphysical ghosting in Course in Miracles groups. For everyone there in the Course group, and imagine for the new people that are coming in for the first time, and they get to be here, everybody is an illusion. People start to open up emotionally, they start to talk about their hurts, their pains, their wounds and everything, and then get over to this illusion. That's like trying to come in with Z when you're just starting out with ABC. You have to start out where you emotionally perceive yourself and then open up to being shown another way of looking at everyone and everything, but that's what the mind training is about, the entire workbook is designed to take you into that experience. So it's the same with the body, the body of a brother, sister's illusion, sicknesses illusion, you know, these are pointing to a very, very high state of mind, but to presume that you are at that frame of mind and to speak as if you are at that state of mind if you do not experience it that way, there would be a lack of integrity even in that. So I think it's good that you're raising this because it's more of just taking an honest open look at what you are feeling and what you are thinking and what you are believing and then being willing to be shown another way and keep coming with that little willingness with the Holy Spirit and tapping into the Holy Spirit over and over and over and moment by moment. That's much more authentic than trying to use a metaphysical ghost line and I have heard people, you know, who go into hospitals and somebody's in traction, they're banging on the cast or what, you know, why did you do this to yourself and different kind of things, you know, this isn't really following guidance at all, you know, it's like taking something that you read in a book and trying to, you know, apply it verbally when psychologically it's not an experience at all and what does that really teach if you're trying to teach something verbally that you don't have a full faith and belief in or a full awareness of?


Доброе утро. В 2017-м Nobel Prize in Medicine был отмечен к Джейфри Холл, Майкл Росбеш и Майкл Ян для исследований молекарных механизмов, контролируя циркадийный ритм. Но давайте попробуем понять, что это означает для нашей нормальной жизни. У вас ли вы встретили джетлак? Я встретил его довольно часто, потому что, чтобы идти домой, я должен летать от Мюник to Krasnoyarsk, и это 6-часная дифференция в времени между этими два сетей. Поэтому, когда я приезжаю, обычно с утра, уже солнце, но я хочу спать. Конечно, я спал всю ночь через мою трепетку, но в следующие несколько дней я бы хотел спать через деньгами. И мне нужно несколько дней спать. Так что что-то из-за меня не понимает, что я actually moved from Мюник to Krasnoyarsk. И люди говорят, что эти вещи, которые внутри нас, говорят, что это время, сейчас есть циркадийные ритмы. Циркадийные ритмы примерно 24 часа, и это характеристика для многих живых организмов. Это вещи, которые эти организмы работают в зависимости от деньгами. И одна из примеров для таких вещей есть открыть и закрыть флоры планы. Какие-то планы, которые открыли флоры в деньгах, и они закрыли их в ночь, которые make sense, потому что в ночь нет поленейтеров для этих флоры. И это важная вещь о циркадийных ритмах. Они нужны для организмов, чтобы оптимально реагировать к кондиционам environment, которые в зависимости от света и темнота. Наверное, в первом случае, когда циркадийный ритм был описан в деталях, он был сделан по французским исследователям, который был действительно астрономел 300 лет назад. Он работал с плантом, называется Мимоза. И эта плана открылся в деньгах и закрыла их в ночь. И что этот исследователь делал, он поставил Мимоза в ночь, и когда он обзорил, что в ночь планы все еще закрыли, и этот цирк снова и снова открылся в ночь и открылся в деньгах, и закрылся в ночь, но это в полной темноте, он не знает, что это день или ночь. И это значит, что есть что-то внутри плана, которое говорит если это день или ночь, какой-то внутренний цирк. Но что бы это было? Что бы это были физические базы для этого внутреннего цирка внутри нас? Чтобы try to understand it, Let's go to some basics of our life. We know that us these are our genomes. Genomes they bear all information about our body. And genomes they are DNA, and this DNA includes genes. And basically sets of our genes it says who will be us. Some set of genes could make us humans. And some set of genes could make the one fly. And genes they bear information about proteins. And proteins are molecular machines, which perform most of job in our body. So a set of proteins inside a cell it is set of capabilities of a cell to do something. Inside the cell genes are stored in an isolated environment in a nucleus isolated part of the cell. And this makes sense because it's good to store this important information in some safe, isolated environment. But it is also some issue because proteins are synthesized outside this environment in the cytoplasm. So somehow information from genes inside nucleus should be transferred to the cytoplasm. It should be kind of a messenger for that. And it exists. It's actually molecules of RNA which are synthesized on the genes and then when they are transferred to the cytoplasm, they are using information written in them proteins are synthesized. So the one could hypothesize that if we have kind of internal clock, then maybe it could be some genes and proteins for this machine. But how can we find these genes or genes responsible for internal clock? The one could mention the quite easy way to do that. Let's just disrupt all genes one by one. And then look on the animals with these disrupted genes. Maybe they will lose their internal clock. And of course to do the thing like this, it's good to work with some kind of easy kindle animal. And such an animal, for example, is a fruit fly drosophila. We can feed drosophila with a chemical which disrupts DNA and then in the offspring of this drosophila we will be able to find flies with different single genes disrupted. And then we can analyze these flies for their behavior. The good thing about this experiment is actually most of our human genes they are very similar to genes in a fruit fly. And it is quite probable that if we will find some gene responsible for something in a fly human will have the same gene responsible for a very similar function. And this was made by Seymour Benzer and Ronald Kanopka in 1971. Actually they analyzed flies with different genes disrupted for their behavior. They used infrared camera to analyze movements of flies and they generated these kind of tracks for them. So here each track is movements of individual fly and if this is just a straight line it means that fly was not moving and when we have these black peaks it was movement of this fly. And for normal flies we can see characteristic 24-hour pattern. Actually at night they do not move mostly. They move during the daytime when they don't move again at night and so on. This is the normal circadian rhythm of motor activity of these flies. But they find the flies which they are not moving like this. Basically they are asynchronous without this day-night cycle. And they found the gene disrupted in these flies. They called this gene period. Unfortunately Seymour Benzer and Ronald Kanopka could not share Nobel Prize because they died not so far. So okay we have this period gene we know that it is responsible for internal clock. But what does it mean? What does it say to us what is the basis of this internal clock? Let's go to basics again. So period gene is a normal one. So it means that period RNA is synthesized on this gene when it goes to the cytoplasm and period protein is synthesized. Researchers made quite important observation about that. They observed that during the day when it is a light time there is a lot of period RNA but it's almost nothing about period protein. And vice versa at night when it is the darkness it is a lot of period protein in the cells and almost nothing about period RNA. And this observation led to the hypothesis that actually period protein could go back to the nucleus and then it could stay away from its own gene. There is a normal half-life time for each protein and it means that when period gene will be blocked with time period protein will be degraded just by itself by itself it will be disrupted. And then period gene will be active again because period protein will not block it anymore and period RNA will be accumulated again again. This set of events leads to the cycling of period protein concentration in the cells. So during the day concentration of period protein is minimal when it raises at night when it goes down again during the day and this is the basis for our internal clock. And actually period protein can block activity of dozens of other genes and then cycling of period protein concentration leads to the cycling of concentration of dozens of other proteins. And this discovery was made by Jeffrey Hall and Michael Rosbush. The next piece to the slide was added by Michael Young. He discovered another components of the system. He discovered a timeless protein which actually needed for period protein to block activity of period gene. These two proteins act in one complex and they should be together to block activity of the period gene. And this is a quite important component because this timeless protein is an input for light in the system. Because there is another protein Cryptochrome, which is sensitive to light. And then it senses light. Cryptochrome induces disruption of timeless protein. In this case timeless cannot anymore help period protein to block activity of the period gene. Period RNA accumulated again and then this switch to the condition like during the daytime. And the good thing about all of this is that humans have a very similar system. We have also period protein which cycles and which does a basis for our internal clock. So that's the way how I finally adjust to a jet lag actually. But all these discoveries they lead to quite a big paradox. Because it means that each cell in our body has its own internal clock. When billions of clocks in one body, how are we going to be synchronized then? And the answer is actually our brain performs this job. Our eyes are sensitive to light. When the signal about light is transferred to our brain and specific part of our brain called hippocamp synthesize molecules which goes through our body through blood vessels and which noon the clocks in all our cells. And one of the main office molecules is a hormone called melatonin and it was shown that there is cycling of concentration of melatonin in our body dependent of the daytime. Concentration of it is minimal during the day, it races at night when it goes down and so on. And it was shown that proper melatonin cycling is important for general health immune response and our psychological stability. So now we know much more about this our internal clock and these discoveries which were made by Nobel laureates in United States since late 60s to late 90s led us to a big picture of understanding our internal clock from different sites. And knowing all this I would like to say again how it is actually important to sleep a knife during each day the same time to have our external life synchronized with our internal clock. Thank you. I will be happy to answer to your questions. Please raise your hand if you have any. So how about blind people? So I would say that we can distinguish two categories of blind people. There are people who are absolutely blind and they do not feel any light. And when it was studied they still have this internal clock they have cycling of melatonin but it is independent of the light and darkness outside. And that's quite interesting that we have this 24-hour cycle of melatonin cycling of melatonin concentration but completely blind people they have around 25-hour cycle. So still there is something which differ in them from us seeing light but still they have these cycles. And people who are not completely blind they do not see but they still feel some light just. Because the fact that it's some lighting still can adjust the system. And the second question? As far as I understood that this system works like through photoreceptors so what about people living in Canadian countries where they can't find any sun during the whole winter? How is this regulated so far? You know the really exciting thing is that to start with 24-hour circadian rhythms for an animal it's even not needed to be exposed today at night. For example in experiments with flies it was shown that flies living in a complete darkness for generations they still have these 24-hour cycles they do not move mostly 12 hours when they move 12 hours and so on. So cycle is like inside in this protein system and you will have it anyways it's evolutionary concert but if we have light we can adjust this cycle but even if we do not have light it still will be existing. Oh maybe just one question there. You? So when we sleep and we have like the lights on this gets also alters our cycles because our eyes can see still some light this could affect our rhythm cycles. You mean if we have some light when we sleep? Yeah it's going to be bad actually because this system is sensitive to light and even if we got sleeping I guess it will be not a proper sleep actually because you know when we sleep it's of course condition of our brain but also there are physiological processes which should go in parallel with another pressure in our body and so on and these things they are more linked not to the nervous system but to these harmon things and so on but because of light you are going to get not proper suction of melatonin already and then probably you will sleep but physiological processes during this time will not be characteristic for sleeping and then you will not have them during the night and you will not have proper rest afterwards.


So I'm Andy Pittman, I'm the director of the ARC Centre of Excellence for Climate Systems Science based at the University of New South Wales and my personal research is focused on terrestrial processes and their representation in climate models. So the basic underpinning laws that climate models are built from includes the basic Newton's laws of motion, conservation energy, conservation of mass, basic physical principles that physicists discovered a very long time ago. They're the basic underpinnings of the core of a climate model. Then on top of that you build parameterizations or representations of processes which need to be resolved at scales that we can't explicitly model in the climate models. Things like clouds, things like convection in the atmosphere, things like eddies in the ocean, things like land surface processes and a lot of the challenging in modeling the detail and the regional patterns of climate come from how we represent those parameterizations. We don't so much tune parameters in climate models. What we do is take observations, learn from those observations how processes work and then we build mathematical representations of those basic processes into the climate models in the form of computer code. All a climate model is is a million lines of computer code running on a really really big computer system and that computer code is a suite of algorithms built from observational data where that exists or basic physical principles where we lack the observations to understand the detail process. But when you run a climate model and you get data from it you always reintroduce observations at that stage to evaluate the model to check the model. We always analyze the model results to find weaknesses and there are always things that work better in climate models or worse in climate models and we use observations routinely to tease out what does and doesn't work well in the models and of course focus on improving those things that don't work well. The things that climate models struggle to capture well would include some extreme events. They struggle with the location of the storm tracks. They struggle with the detail of cloud fields. They struggle with some major challenges we don't really represent at all the processes which might trigger a abrupt climate change. So methane release or permafrost melt although some people are beginning to work on permafrost. So I think it all depends upon your perspective if you ask what do the climate models struggle to represent in terms of the simulation of whether it will warm for a doubling of CO2 my answer would be nothing at all because they do that really well. If you ask what the climate model struggle to predict at the scale of a region and its response to doubling of CO2 in terms of rainfall lots of things. They don't get the details of the clouds the convection the rainfall processes the detailed synoptics blocking a whole range of things because the spatial resolution that we use for climate models is probably too coarse to capture a lot of those kinds of key phenomena and so one of our grand challenges in climate models is to dramatically improve the spatial detail that climate models use and that's really a computational problem. We just need bigger supercomputers to to really resolve the detail of those things. Look if you go to observational data sets of the global patterns of anything pressure rainfall temperature you won't be able to tell which is from a model and which is from the observations.  So at the large scales they are outstanding there's probably no parts of a climate model of a modern climate model that still reflects what was done in the 70s almost everything's been rebuilt or rewritten I think. The resolutions increased from something around 700 by 500 kilometres to 100 by 100 kilometres. The detail in the vertical has increased dramatically, oceans have been properly and fully coupled, the land surface has been completely revised to incorporate a whole suite of processes, sea ice models have improved dramatically, cloud parameterizations have improved, we've resolved most of the water vapour feedback problem. It's like asking what's the relationship between a Formula 1 Grand Prix car in 2014 compared to 1970 and the answer is there probably isn't a single widget that's shared. Because climate models are built from the basic physics they should be applicable to any environment so you should be able to take a climate model run for the earth and use it for a different planet and people do do that. Similarly because the basic physical processes and Newton's laws of motion don't change and conservation of energy and mass don't change with time in any context that we understand it anyway. The climate model that's built for today ought to be able to do the past, paleo climates for instance and the future and indeed they can. They're not by any stretch of the imagination perfect if we try to simulate the last glacial maximum or 30 million years ago but they do capture an awful lot about long term past climates and indeed there've been discoveries around things that happened in the long-distance past which have been hypothesized using climate models and then discovered from observational data. So if the climate models do a pretty decent job of simulating the long-term past climate change and can simulate the 20th century extraordinary well we have considerable confidence that they can simulate the near future extremely well and by near future I mean out to 2040, 2050. As you move forward into the future if mechanisms begin to develop that are outside of the observational record the observations we've used to build the model in uncertainty increases. So for instance we don't represent the processes that might trigger a abrupt climate change we suspect there are things that might evolve in the climate system that aren't in the observational record and whether or not we will capture those nasty surprises time will tell. So paleo climatic modeling is extremely useful because it tests the models for very large changes in the Earth's climate in the past. The problem is they're not necessarily good analogies with the next 50 to 100 years. Paleo climate mostly was overtly driven and driven by movements of constants. Under time scales of 50 to 100 years we're not expecting massive changes in solar luminosity if there were we have bigger problems than global warming and we're not expecting Africa to suddenly move a thousand kilometers west. So the triggers and the drivers of paleo climatic change are profoundly different to the drivers of human induced climate change and so it's useful to test models but it's insufficient it's useful but insufficient necessary but insufficient. There's a whole variety of reasons why we're confident in in the skill of climate models for the problems that they were designed for. First of all they're built upon physical principles and those physical principles are known unless of course that Newton was stupid which I don't think he was. So we have basic fundamental theory not sourced from climate science but sourced from basic chemistry basic physics basic biology that and applied mathematics that says the core of climate models is sand. Secondly of course they use routinely in other applications like weather forecasting and so we have effectively can evaluate a lot of our science routinely and weather forecasting is becoming increasingly accurate irrespective of what some of your listeners might think. If they actually write a diary of a five-day forecast and then check off how those five-day forecasts evolve they'll find that they are shockingly accurate nowadays. So that's the second test. Thirdly we routinely test our models against observations over the last century and earlier and they do extremely well in that in that respect and finally we can test our models against perturbations so we can for instance simulate a volcanic eruption for example and check that the climate models respond appropriately to to what a volcano does to the atmosphere. So there are multiple lines of evidence and they all point to the climate models being reliable for what they were designed. So on longer time scales so not five years but for periods where the initial conditions aren't critical so on time scales of decades and longer they're very very good and for problems that are at larger spatial scales so continental scales and above they are I think extremely reliable up to 30 to 50 years into the future. As you move further into the future I become less confident so I think they are a good and indicative guide for what the climate might be like in 2100 but I wouldn't be absolutely sure and I wouldn't be sure not because I think they're overestimating the response but I think they lack a suite of mechanisms that might amplify global warming and so I suspect if anything they are underestimating the scale of change out to the end of the century but this science isn't clear on that so I'm being a bit speculative. Secondly spatial resolutions at say the scale of Queensland or New South Wales the detail isn't in the models because of the computational cost so as you move down to finer and finer scales as more and more mechanisms are interwoven to give you the regional pattern of change I think the climate models become things that we're less confident about. Finally the things that are not based upon well understood physical principles so changes in agricultural yield require a whole variety of things to be done well ranging from soil moisture and rainfall and temperature and lots and lots of all lots of things that interact. When you need to get dozens of things right finer spatial scales out of a climate model to give you an impact then you should treat what the climate model tells you about that impact very carefully. There's a lot of myths out there on what we think or how we build our understanding of what will happen in the future. There are many lines of evidence that are used to understand how climate might change in the future and if you could take climate models away we would still be just as worried about the future climate. Climate models merely inform and embellish and add color and flavor to the future of the climate the projections of future climate but we would be just as worried based on theory and data. Climate models are one strand of evidence for future climate change but by no means do they underpin our concerns what underpins our concerns is physics. So there's a lot of research out there that gives us insight on on climate sensitivity. It ranges from some fairly sophisticated analysis of paleoclimates through to some rather simplistic approaches to understanding how much the climate may change. Climate sensitivities how much the planet will warm in equilibrium to a doubling of CO2. The range in the last IPCC report was 1.5 to 4.5 degrees. I think the 1.5 will turn out to have been overly cautious or optimistic from the science community. I think the right number is in the high 2s or low 3s and I base that upon multiple lines of evidence. The evidence to suggest it's below 1.5 is is very arguable I think. People need to understand that there's a multiple line of approach to the peer reviewed literature. So somebody can write a paper and get it through the peer review process that's the first check but the much tougher check is time and the test of time. So people will publish papers that suggest climate sensitivity six degrees or 1.3 degrees and both of those are big claims needing strong evidence and critically independent reproduction. And what we tend to find is the big claims that climate sensitivity is very low or very high turn out over time when checked and reproduced and rerun and reanalyzed don't stand independent scrutiny and that's the proper process in science. There's nothing wrong with that if you come up with the really strong piece of evidence that the climate sensitivity is six degrees and you can get that published good for you. But it's then a requirement that the community do their best to destroy that. And if they can't destroy it then that's really scary in the case of six degrees and really good news in the case of one degree. But so far as I know every single time somebody has come up with values very low or very high subsequent analysis has demonstrated that they're wrong. But I do like the scientific method because a lot of people don't understand how it works they assume that if you've published a paper the papers right. Obviously everything I've published is right including the things that I have contradicted in subsequent papers. Science evolves and the bigger the claim the bigger the attack on the claimant and it's right that that's true. It's good that that happens. One of the really emerging challenges in climate science is the question of extremes. Weather forecasts are built to capture extreme weather or to predict extreme weather and it's really really hard. It's particularly hard when an extreme is a combination of multiple events occurring simultaneously. And climate models cannot model or simulate the detail of extreme events. So for instance a tropical cyclone. I don't think climate models tell us very much about what will happen to tropical cyclones because they're just not built at the spatial resolution that enables us to capture the detail. And in and also things like blocking where a high pressure system forms and holds the climate in place for a significant period of time. Commonly leads to extreme conditions particularly heat waves and drought. And climate models just don't have the finesse the spatial detail to capture the mechanisms which drive those sorts of things. So there has been a huge growth in analysis of climate models and extremes but most of it is the statistics of extremes. So what would be the probability of particular events occurring in the future? Not what would a specific event look like in 2030 or 2050. So when you're looking at the statistics of extremes I think climate models have some skill in showing you the sign of the changes but I'm not overly confident they yet have the skill to give you the detail around how extremes will change. The problem is that whichever extreme you look at seems to get worse in the future in part because of the intensification the hydrological cycle and the just the background warming that we see from CO2. So I think we have a good understanding that a lot of extremes will get worse but of course some extremes will get much rarer like extreme cold at least in Australia. It's not necessarily true that extreme cold will get less common in higher latitudes where you see an increase in the strength of the storm tranks and more variability for instance bringing Arctic air down over parts of North America and Europe. Global warming doesn't mean everywhere has to warm. The relationship between global warming and the hydrological cycle is something we're beginning to understand much better. Basically you can start at a whole variety of points in the hydrological cycle because it's a cycle. If you start with evaporation if there is more energy there is more energy to drive evaporation. So if you increase CO2 and you increase downwelling infrared radiation there's more energy in what we call net radiation and it's the net radiation that's used to drive processes like the turbulent energy exchange with the land the latent heat flux or the evaporation and the sensible heat flux which directly warms the lower atmosphere. So if you have more energy you drive the evaporative processes faster and you pump more energy at the base of the atmosphere in the sensible heating that drives deeper convection more moisture being driven up higher into the atmosphere that tends to increase the probability of rainfall and more intense rainfall and you can see how a cycle begins to form but basically it's an acceleration or an intensification of the evaporative processes. So we suspect that areas already susceptible to drought will end up in drought for longer periods because you tend to see a situation where the evaporative process driven by the energy sucks the water or drives the water out of the landscape a little bit quicker, not massively quicker. We're not thinking this process would drive a one month drought to be a 20 year drought but if you have more energy and there's water available that water evaporates a little bit faster. If you evaporate the water a little bit faster you dry the landscape out a bit quicker and that tends to intensify drought but the major driver of droughts across the landscape is the larger-scale synoptic patterns and the modes of variability like ENSO and El Nino and El Nino cycles. So the key regional drivers of drought are the large-scale modes of variability and what I was talking about in terms of latent heat and sensible heat drying out the landscape is if you like the detailed brushstrokes on a larger canvas. So you have to look at the system in its full coupled glory to understand the difference between a drought that's occurring becoming slightly more intense because of evaporation as distinct from the large-scale patterns driving more frequent droughts and those are two quite different mechanisms. So if you're in an area that's already susceptible to drought it may well be in some years enough to drive so much deficits which are problematic for agriculture or for water resources in general beyond a threshold such that they're problematic. Also if you have a general drying when it does rain that water doesn't necessarily make a substantial difference for agriculture or for water resources. So a much drier landscape where everything's in deficit if there is rainfall that landscape will suck up that rainfall and hold it which is a problem for for dams and water supply as we have seen in Perth for instance. So a small change in the dryness and a small change in the rainfall commonly translates into a big change in the runoff into dams for example and there's good understanding of the mechanisms behind that. When I was an undergraduate I was offered a choice of either going to Canada to work on land surface processes or France to work on cloud processes my French is appalling so I chose Canada. Okay so my personal area of research is terrestrial processes and there are a huge number of opportunities for bright students to contribute in in this area. There's a whole variety of interesting questions it depends where you sit. One is how do we parameterize or represent urban landscapes in climate models. Urban environments appear to amplify some extremes. Those aren't represented in the climate models because they're too fine a spatial scale. We're now moving down to their scales that we need to resolve those landscapes properly and how to represent urban landscapes in a climate model so we get the right moderation or amplification of a global warming signal is very important. Another one is how do we represent agricultural systems in the climate models they act quite differently to natural landscapes for instance they're harvested and they have a quite a different phenology in terms of the way that they interact with the atmosphere and how to do that is a real problem. Next is something around soil carbon soils store a vast amount of carbon and there's good evidence to suggest they'll store less carbon in the future because microbial activity will break down that carbon more far more quickly and recycle it back to the atmosphere. That's a six million dollar or six billion dollar question because if the soils lose their carbon as a consequence of global warming it's a massive positive feedback. If the soils hold on to their carbon as well as they currently do or even increase their ability to store carbon that's a negative feedback on global warming and while we're pretty sure now we have a positive feedback we're not sure of the scale of that feedback it's extremely messy and complex to represent those processes and it's something we have to do much more skillfully and the last is the whole issue of human modification in the system things like irrigation damming of rivers how those flow into the oceans because that impacts on the ocean circulation that whole issue of how humans interact with the energy carbon and water cycles is a massive challenge that we're only beginning to chip away at. Most of us who work in the field think that using soils to store carbon if we did it really well and really actively and on very large scales might enable a country to attain its current levels of soil carbon. So by default we'll lose soil carbon we're losing it by erosion because massive amounts of soil are eroded annually across the Australian landscape because filing practices and land clearance and a whole range of other things. So the default is we lose soil carbon over the next 30 to 50 years at a significant level. If we really actively work on that system we may be able to hold the current soil carbon in place. The notion of using soils as a carbon sink to offset global warming is optimistic. So traditionally I would have a conversation with them. I've started depends who they are but I've started saying look everyone's entitled their own opinions but not their own facts and if you happen to want a conversation around why the science is now clear and robust I'm happy to have that conversation with you but if you've already decided that we're a bunch of global conspirators who've managed to orchestrate a complete conspiracy that has convinced most of the world's governments of a problem that doesn't exist I probably can't do much for you. Climate scientists are trained in maths, physics, computer science, biology, chemistry and as most people know people trained in those fields have ordinary communication skills typically. Our definition of success is our ability to write code or do a chemistry experiment or whatever it is. And I've recently spent some time assessing graduate students what's called the Monash Foundation and a number of the students who come in to be interviewed for scholarships are from law and they have spent lots of time mooting and debating and their ability to communicate, to succinctly summarize their interests, their desires, their vision is phenomenal because they're practiced. So one of the things we're trying to do is get our graduate students into a much more personalized program where they do talk more frequently. We're trying to build opportunities for them to be trained a bit more in science communication because I think it's critical and the answer to the question is practice and understand that the first time and the 15th time, the 50th time you'll probably do it not very well but in the end you'll develop some skills. I also think we have to read more broadly. I do not believe that we can all go and do a second degree in science communication or in psychology. Climate denial is increasingly understood by the psychology community and there are some fairly accessible books ranging from things like Naomi Oreski's book on motions of doubt which gives you a really good insight on some of the problems but there's also books in psychology and there's even great posters which show you the psychological trick that someone trying to deny climate change is using and why it's falsifiable. It's worth knowing about those things so you can see the techniques being used against you but it does require a little bit of a broadening of the training that we all have and we have to be very careful with that because what we really need is really committed bright students doing climate science not trying to be everything to everyone so it's a balance but but at least engage in some of the the basic background to why people have these scientifically invalid views is a useful thing to do. I think I'd simply say that if we're emitting 10 billion tons of carbon dioxide in the atmosphere per year and you don't think that can have an impact you need to look around you and look to see what happens in a city like Beijing when a fraction of that level of pollution is put into the atmosphere people can't breathe in summer so we can look around us and if you open your eyes you will see changes going on around you that are clearly demonstrable evidence of climate change and the consequences and if you choose not to look there isn't probably very much I can do for you.


So thanks for for the invitation and on the purpose that I'm the only one separating this talk from Lance. I'm going to talk about understanding water interacting with different surfaces. Originally I was going to talk about photo catalysis and electrochemical applications but I focus mostly in the electrochemical application setting. So in terms of what we are interested on is really being able to simulate using first principles an electrochemical cell and that we are still very far away from doing it for many reasons and starting from the fact that we need to do similar to in thermal transport long simulations of initial molecular dynamics simulations and large systems. But there are other problems that separate us from getting there and they are related to the understanding of the systems that are part of this electrochemical cell and in particular the key element is liquid water which is always part of these cells and the simulation of the metal interacting with liquid water. So ideally what we want to know is the correspondence between the macroscopic voltage that we applied to an electrochemical cell and what is the actual interfacial charge that occurs at the surface of the metal interacting with water or with an electrolyte which is liquid water containing different ions. And beyond there of course is what is the actual structure of the electrochemical double layer that occurs at that interface and how and applying a voltage to the system modifies such a structure. And so what we aim to simulate is some sort of interface with water and a solid in this case is a metal and we need to have a very accurate representation of the two systems. We need to bring them in contact and the questions that we want to know to start with are what is the structure of the full struggle of all the molecules so the liquid itself at the interface whether it occurs or not a charge transfer between the two systems whether that is modified applying a voltage. And so we are going to start from the most simple system that we can imagine which is just one single water molecule at the surface of the metal. In this case this particular simulation is for palladium and there have been many many studies about what happens what's the orientation of that water at the surface of the metal depending on what is the metal that we have but the bottom line what characterizes interaction is the fact that of course what the metal is going to screen the charges of the water is going to create an image charge at the interface and here I'm showing such image charge for a single water molecule so in blue what you will see is a depletion of electron so a locally positive region and in red is an accumulation of electron or a locally negative region. And so indeed what happens with water absorbs some such a metal is that you have something that is like a mirror and water interacts with the metal by forming pseudo hydrogen bonds indeed you will have a hydrogen bond of the oxygen with the with the local charge underneath and another and of course it's sort of pseudo in that there's no real hydrogen in here but the depletion of electrons represents the the local charge of a hydrogen and it's actually the same on the other side however I mean according to this what what we will expect if everything is electrostatic is that indeed if you just have one single water molecule the most stable configuration will be that where the water molecule is placed with the dipole moment perpendicular to the surface so this electrostatic is the most favorable interaction for a dipole with its corresponding image charge and what you're seeing here indeed is if we just have a classical description just just point charges and describing the water molecule point charges positive and the hydrogen and negative at the oxygen interacting with a perfect metal that creates the corresponding image charges the most favorable configuration is this one in here which with the dipole pointing up the second metastable configuration is this one in here and that's the transition state when the molecule is placed horizontally and we include semi-classically meaning that we allow for polarization both in the in the metal and the water molecule but without any chance transfer the picture is slightly modified we see that we acquire some other semi stable state at this structure which represents the position of the lumpers of the water molecule but you still find that the corresponding up and down dipoles are the most stable however it happens to be that once you include everything including the full charge redistribution and inter and charge transfer between metal and water what you see is that this horizontal position is the most stable and the reason for that is that the pilot repulsion between the electronic cloud of the oxygen and the image charge in the metal is basically dominating the interaction and favors this and this configuration and so if one goes and we want to do now simulation of large systems plenty of water molecules and metal there is the the question of what how should we do it using fully classical semi-classical all fully ab initio simulations and so some of the largest simulations that have been done were done in the group of David charler and what they did is basically using a semi-empirical force field with where water molecules were described rigidly so they are in a way purely classical simulation there's no flexibility on the molecules and everything is point charges interacting with that with the metal and with a force field that actually includes polarization or the creation of image charges in the metal and this is the the model use and is it was a model which is in a way fitted to reproduce ab initio simulations in particular the absorption of water on platinum with a monolayer both geometry and energy but never a full bulk system and so what was observed in the simulation of this system is that indeed depending on what is the determination of the metal so either platinum one zero zero platinum one one one interfaces the the structure of the water layer at sort of the interface is very different and in particular what they described is that in one zero zero interfaces what one obtains is some sort of a hydrophobic water layer what water gets so well bounded to the metal forming bonds with the metal within the water molecules in the metal such as it doesn't favor the formation of further hydrogen bonds with the with the upper water layers and so what you guys is a highly hydrophobic layer and not because of the not on the metal itself but on water itself and and if we actually go to the one one one structure what happens now is that we have much more freedom for the water molecules to absorb either horizontally vertically with hydrogens pointing up or down the layer and now we open the path to many more hydro and bonding structures with the water and that automatically makes this this layer very hydrophilic but of course here is the question of how much is this correct if from what I said before and the fact that there is not actual chance transfer interaction described in the system and and no no effect of Pauli repulsion of or any other quantum mechanical effect that can occur on all and also what is the the effect of further polar isasol in the liquid water and so the first thing that we did is precisely study a system which is much smaller than the one done by the group the Berkeley group but in a way much more expensive from the point of view of simulations because we we have to simulate the metal and the liquid everything have initial and so there's a I mean this is the summary of a lot of work and of course a lot of these simulations will depend on what is the initial structure that you have for the for the water so first of all we start doing equilibrations using classical force fields and then once we don't different equilibrations we move we switch to quantum mechanics and within quantum mechanics we also have the the question of what how do describe the liquid water which is we all know and one of the the systems that is most difficult to describe at the standard with the standard DFT approaches and so I'm gonna I'm gonna present which are the results and hopefully that will give us an idea of why why these systems are complex and in part also associated to the fact the liquid water itself and is close to a somehow a critical behavior which give rises to structures which largely depend on what are the conditions that occur and the interface between the the water and the metal so if we look at the results of these simulations and and we look at what is the order in of the first layer interacting with the metal what we see is that we actually can have two different types of order one which we call highly order where you also here basically in in red is the density of oxygen and in blue is the density of the hydrogen and this double peak is characteristic of this sort of double layer and you see these two peaks which corresponds to hydrogens which are pointing towards the metal or pointing outside of the metal and then you can also have the structures where indeed you have a much more favorable configuration for molecules with most of the hydrogens pointing towards the metal and so that probably on itself says little about what's going on but it's easier we go now and look also what happens in the in the in the plane and so this structure with the up and down type of positions is a really very order structure so this thing here is the is the unit cell and everything else is pretty boundary conditions and what you see that of course is that indeed water is absorbing forming what is so called a two-dimensional ice type of layer and that structure is very stable and very order and very what we call low density type because indeed is is relatively close to to a very hydrophilic and hydrophobic layer if you go to the other side where we had a a favor of larger hydrogens towards the metal and what you see is that there's much more disorder there's much more mobility in the two systems and and what I'm showing here in comparing is what happens whether you simulate using a standard gga approach or when we use a Vanderbilt's interactions and it seems like indeed the Vanderbilt's interactions modify a lot the structure in the system however that is not really the case what is occurring in this in the system if we we look at the full detail of the simulations is that so this was the the sort of one type of layer order layer two-dimensional ice layer and this is much more disorder more high density type these are the results we do just a pb simulation so without Vanderbilt's and with Vanderbilt's and what of course is that both of them are actually giving the same type of structures but the Vanderbilt's one has a very fluke 20 type of domains so we as what we are seeing is that that we don't have a unique type of structure forming at the interface you have domains which intercombelled from one another and the only difference between the Vanderbilt's and non-vanderbilt's is the fact that this is very stag and the diffusivity is much smaller and we don't have timing scales long enough to explore the motion between the two domains and so if we look at a different also this of course with palladium if we look at gold and in this case the chance transfer between water and gold is much smaller than with palladium so the description of this model should be much closer to to the classical description so it's very similar to platinum and indeed what we see is that the results for golds are very close to the results that were described in the in the Berkeley Groups paper and where we have a very hydrophobic layer followed by a very hydrophilic structure and so how why is this relevant where it's relevant because if we want to understand what is the the the change of work function of a metal upon salvation we need to know what is the actual structure of the layer and the layer happens to be very dynamic and very different in nature so we want to calculate such change of work function what we need to do is basically calculate what is the work function of the metal in vacuum and then the change of it which is due to the dipole of the dipolar structure of the of this of the liquid water layer plus the polarization induced by by the metal and that polarization is basically can be decomposed further into the image of the metal interface the polarization of the water and then the net charge transfer between the two systems and so we do that analysis what we see is that indeed um depending on whether we have one type of order or the other type of order we are going to get so this is for palladium in the case of palladium we have the the the first order that I described with the bilayer has an eight dipole which is pointing out from the surface and that will decrease the work function of of the solvator surface and the other type of order has an eight dipole which goes towards the surface and that increases dipole the sorry the the work function and in the case of gold you get an average and this actually has these two are very large so these two dipoles are large and opposite directions in the case of gold you have a small dipole and always out which is a net decrease of the work function so when you look at experiments indeed you can have before I go I go to that just one extra thing which is and this was already shown by the group of growth they were actually only looking at single bilayer so not a full liquid layer and as a function of the metal and what they were saying is that depending on how is the the bilayer which can be with with hydrogen down or with hydrogen sub the net change of work function decreases as a function of the the actual natural work function of the metal so what we are studying is for gold and for a palladium which are quite extreme cases gold and has a very small change of work function and palladium are rather large one and so so if we were to compute what is the net this is the the delta work function so basically what we are calculating is the change on work function and after a salvation of water without salvation and and so what we see is that and this already includes averaging for over the two structures is that is relatively large for a for palladium and relatively small for gold there is a change of signing gold whether we use a pbe or van der Waals but in both cases what happens to be is that the one that compares much better with experiment is the is the van der Waals results which are which are sounding here so this is the solvated experimental wave function and and this is with van der Waals so relatively close and this is this is for gold but what I want to say is that in order to obtain these these numbers and one cannot just simply do the simulation of one one single monolayer or one needs to actually do a long molecular dynamic simulations but they correspond to really average of these two type of domains and and the connection I wanted to make to liquid water is that these two types of domains are actually in a in fact we believe related to the to the underlying water criticality of having a low density and high density types of fluctuations which they are enhanced by the interaction with the metal now I told about electrochemistry and of course what we say that indeed what we want to know is what's the effect of applying a voltage over this and which I'm not doing just yet not even here and so the the next thing we did is actually charging our surface in the standard way of charging surfaces and or charging calculations in in in a Venetian simulations which is just adding a net charge to the system with a compensant compensating underlying background and then relate that to what will be the situation with an applied voltage so so doping with electrons or charging the surface with electrons will correspond to a sorry this is charging the surface removing electrons will correspond to a negative voltage or in charging with electrons to a positive voltage and what we see is that indeed charging the surface is what it tends to do is to even enhance those two different types of structures it makes an either more order layer for in the case of of positively charged and in the case of negatively charged in favor so much more disorder layer now in reality what we want to know is exactly what occurs if we have an applied voltage which we cannot do and so what we did is basically propose to couple the standard transport non-equilibrium rinse rinse function formalism that is used to to study electronic transport in in systems and nano systems so apply exactly the same formalism but where our scattering region now is going to be the actual so-called electrochemical cell which is metal plus water in in between and so we can actually now establish apply a voltage and taking out of equilibrium the the Fermi level of the two sides of of our cell and in the standard non-equilibrium transport formalism so as we say this arrangement and is the same as what we encounter in electronics transport we have a central scattering region couple two electrons and now these two are going to serve as our reservoir and in principle the voltages that we apply and of course we can actually match now to experiment and the other thing that we can do so that's the description of our region of course the scattering region does need to include a quite a large section of the metal because whatever charts transfer that occurs between the water and the metal and needs to be accounted in the scattering region whereas the electrons are supposed to be already in in equilibrium and far away from the from the from the scattering and the interactions with the water and so we can do this and we can even now as long as we ensure that we don't have and that's the actual system that that we can simulate and we can do this and we can also compute the the forces within the the scattering region which are warranted to be correct as long as we have no actual electrical current circulating through the system which in our case is correct because the band gap of of the scattering region of liquid water is larger than eight electron volts and so we can have ionic currents and that those are correctly defined but we as long as we ensure that we don't have electronic currents and we can really simulate this type of setting and so that's what what we really want to do but with this this work was just a proof of concept and to prove that that it works so we started again back with one molecule but now what we are doing is really doing relaxations of the of the structure of this molecule in the presence of an applied voltage so in this case what you're seeing is what is the configuration for a positive voltage so the water molecule is here and as you see that means it's interacting with the negatively charged surface and so one of the things the first thing that that we we identified is that in this the potential energy surface of this water at surf at the surface as a function of the voltage is really far flat and with the voltage meaning that up to voltages is smaller than than one one volt there's very little happening the equilibrium configuration strips shifts very little voltages and it's only when we go to voltages larger than one volt that we start seeing that we are starting changes and in this these are the structures this is the equilibrium is all doing a standard conjugate gradients optimization and so what you see is that it's a very highly asymmetrical simulation with a positive voltage I mean charging with electrodes the surface what happens is that the molecule gets unbound and now of course the so the the molecule basically goes far away from the surface and gets unbound with negative voltage you have a very small change to shift up the hydrogens very little and the the distance between the oxygen and the and the surface decreases slightly but you can see how how asymmetrical is the structure with the voltage and the reason we understand that is actually as I started the talk with and exactly the this poly repulsion between the charts in the water and the metal is strongly bias dependent and so what we did is again do the simulations but now subtract and repeat the simulations with for the for the electrode itself without the water and then the water along with the with the voltage and subtract one volt for each other and so in a way what we are doing is removing what are the effects of the electric field that is coming from the metal and looking only at what are the effects from the from the charts exchange between the metal and the water and so this is with with zero bias and what you see with zero bias basically what you want to look in this picture is the larger the blue region is the larger is the poly repulsion so you have already a very large poly repulsion between water and the metal when you decrease sorry the opposite the less you have it in here the less the less blue region is the the larger the Fermi repulsion and so you see that you decrease the Fermi repulsion when when you apply a positive voltage and you increase it when you apply the negative voltage and and the reason for that is basically because you really change how much is the the amount of chance transfer that occurs between the two as a function of the voltage and so that's where where we are now so this is for both of course which already to start with there is a very small chance transfer so indeed if you look at the numbers in here the iso surfaces are very small so so 10 to the minus three electrons per cubic ounce drum so how much you see the asymmetry between applying a positive or negative voltage in the case of gold is small because it's all to start with there is a small chance transfer between the metal and repeating the same with palladium makes the simulation of this electrochemical cell much more complicated because now the region that we need to describe within our scattering section the region of the metal is much larger because precisely of that charge interaction and this is what we are working on now and hopefully we'll have results for not only for different methods but also for larger for larger cell simulations so I'm going to stop in here and the first thing that that I do want to highlight is that indeed the order of water at biases interfaces it really reflects and the fact that water has this instability of forming a two different type of structures low density and high density and the second is that indeed we can now simulate electrochemical cells using the non-equilibrium response and formalism to describe this system until an external bias and we can do that without having to add charge to the system so really being able to to compare to methodology and so we can now do molecular dynamics as I say as long as we ensure that there is no electric transport through the system which is ensured with systems like this which have large gap and with this I'll finish thank you