Today, I will be leading a virtual tutorial on the INVEST Coastal Vulnerability Model. We will be focusing on a brief introduction to the science underlying the INVEST model and then a live demo looking at the model inputs, the user interface, and also the modeling outputs. My name is Jess Silver. I'm an analyst with NatCap on the Marine team in Seattle, Washington, and I have been working with the INVEST Coastal Vulnerability Model as well as some of the other Coastal Marine models for a little more than 10 years at NatCap. So the INVEST Coastal Vulnerability Model is a model that is used to understand the distribution of risk of exposure to coastal hazards for people living along the coastline. So it's a model that incorporates a number of different aspects of coastal and near shore processes which include climatic forcing conditions along the coast, so exposure to waves and wind as well as potential exposure to storm surge, and then also natural characteristics about the shoreline, such as the shoreline type or geomorphology, the elevation, as well as the presence and extent of different kinds of natural habitats. So the main questions that the model is designed to help answer are questions such as, are there areas in my region that may be more exposed to coastal hazards, specifically understanding where people and infrastructure and other valuable assets are most exposed to coastal hazards in my region, and then also a focus on understanding where marine and near shore ecosystems can help reduce these impacts. The data inputs for the model are rasters and shapefiles that speak to a number of different shoreline characteristics, so I will go through these individually. But in brief, the model takes into account information about shoreline geomorphology, so the shoreline type, about the extent and type of biotic and abiotic habitats, exposure to wind, waves, and potential for exposure to storm surge, coastal elevation, also known as coastal relief, sea level rise, and then lastly, information about social and economic metrics along the coastline. So this is a relative index-based ranking model. So each of the model inputs are ranked from one, which is the lowest contributor to coastal exposure, so essentially the lowest risk category to high, which is a five, and that's the greatest potential for contributing to coastal risk. So we have low exposure, low risk with a one, high exposure, high risk with a five. And each of the inputs is assigned ranks, and the ranks are a mixture of absolute and relative values depending on the different input. And again, we will walk through each of these individually. So the first input is shoreline geomorphology, and so in this case, the model is trying to distinguish between shorelines that are relatively hard to road or wash away versus very soft shorelines. So in this case, we're really trying to distinguish between rocky, gravelly shorelines and sandy, muddy shorelines. And so here this is an input where the user is going to assign absolute ranks based on the different shoreline type. This is often a line shapefile where the ranks are assigned to line segments. The next input is coastal relief or coastal elevation, and in this case, the model is trying to distinguish between low-lying areas, which are going to be more prone to erosion and inundation, sorry, to flooding and inundation versus more steep areas, which are going to be less likely to flood in a storm. In this case, the model uses a digital elevation model or a topographic layer to calculate the average relief for each shoreline segment. And then it calculates the ranks based on the full distribution of elevation data for the region. The next input is natural habitats. So in this case, the model can take into account information about the extent and the type of both biotic and abiotic natural habitats. And it applies a user-defined rank that corresponds to the potential for different types of habitats to provide different levels of coastal protection. So for example, coral reef, mangrove forests, dense coastal forests might be given a rank of one, which indicates that they are more effective at providing coastal protection than habitats such as seagrass or kelp forests, which may provide some protection, but certainly less than the more rigid habitats. The model also can incorporate information about sea level rise, and it does this in a couple of different ways. So if there is a region such as the coast of the United States, the world, essentially if you're running the model over a region where you have spatial variation in relative sea level rise rate across the region, and you have the data to understand how sea level rise varies, you can capture that spatial variability in the model directly. So in this case, we have a number of different relative sea level rise rate trends, and we calculate the ranks based on the distribution of rates. You can also use the model to look at the difference between current and future sea level rise scenarios. So in some cases, either there isn't spatial variability or you want to incorporate both spatial variability and future net rise, and you can use the model to look at that. For reference, we have published a number of papers, including the Nature Climate Change paper here on the right that have extensive discussions about how you can use the model to look at different sea level rise scenarios. So the model also looks at exposure to wind and waves, and in both cases, it uses a globally available data set that is curated by NOAA. It's called the WaveWatch3 model, and it models wind and wave statistics for different points all over the oceans across the globes. And so we use this data set in the model, and you can exchange it for something more finer scale if you desire, but the global data set is totally sufficient. We use it to look at the wind and wave exposure for every shoreline segment along the coast, and then again, the ranks will be generated based on the full distribution of the wind and wave values. And so in this case, the model looks both at ocean waves, so oceanic generated waves, and then also local wind generated waves in sheltered areas. The model also looks at the potential for exposure to storm surges. So in this case, we use a proxy, which is the distance from the coastline to the edge of the continental shelf. The graphic below shows that when you have a long, shallow extent where waves can build up, you are much more likely to see storm surges, high storm surges, than if you have very steep drop offs. So in this case, the rank is generated based on the distance from the coast to the edge of the shelf, where you see a large, shallow extent contributing to greater risk, and a higher exposure rank, and short distances to the shelf, and steep drop offs contribute a lot less to exposure and get a lower rank. So this model is a relatively simple, transparent model, and it is also designed to be able to use somewhat coarse data sets. So there are a number of globally available data sets that can be used with the model, and it makes it very easy, relatively easy to run this model anywhere in the world. So we have included a number of the global data sets with the sample data associated with invest. These include the global data set for the WaveWatch 3 wind and wave statistics, a global map of the continental shelf, globally available DEM, and topographical information. There are other resources that we do not include with the sample data, but that are very good global data sets, including global habitat data sets curated by UNEP, the permanent service marine sea level rise has information about relative rates of sea level rise and tide gauges around the world. And then there are a number of different data sets that can be used for the population and demographic information. This is a schematic that shows a simplified version of how the model is working. So on the left, the first step is to grid up the coastline of the region that you're working with into a user defined segment size. So in this case, the coastline is gridded up to one kilometer shoreline segments. And then each of the different input data, which we just reviewed, are gridded up to the same resolution and they are ranked. And again, they're ranked based on the different approaches that we just discussed. So this could be absolute user defined ranks for some of the input variables and for others, the model is assigning the ranks based on the full distribution of values. One of the primary model outputs is something called the coastal exposure index. So in this case, this is the hazard index that's shown on the far right hand of the schematic. And the hazard index is the geometric mean of all the variable ranks for each of the inputs. So in this case, in the schematic, we've shown the hazard index categorized into high, moderate, and low. So when it comes out of the model, the hazard index is a continuous value from one being the lowest risk of exposure to coastal hazards to five being the highest risk of exposure to coastal hazards. And depending on your application, you can choose to define this categorically, such as high, medium, and low, or perhaps assign a threshold. So the second part of this model is to understand who and what is at risk along the shoreline. So when we talk about risk, we're really talking about the combination of exposure to coastal hazards and vulnerability to coastal hazards. So the components of the model that we just reviewed, which culminated in the calculation of an exposure index, these are the parts of the model that is going to help you understand the relative exposure potential for exposure to coastal hazards along the coastline. And then the second piece of the model is to understand who and what might be vulnerable. And these taken together are risk. So there are a lot of different potential social economic and demographic metrics that could be evaluated when thinking about vulnerability. So a simple place to start is population density. But depending on your application and on your region and on data availability, there are many, many factors that have been studied in great depth that help contribute to the vulnerability of different communities to recovering from and adapting to and preparing for coastal hazards. Now that we've done a review of the underlying science of the model, we'll look at a brief case study to get a basic understanding of how the model is applied. And then we'll do a live demo. So this is a case study from the Bahamas. This was a country-wide coastal hazard analysis done for the Bahamas. And in particular, this analysis was used in post-disaster reconstruction and coastal resilience building efforts following several years of very devastating hurricanes in 2015 and 2016. So ultimately, this analysis was used as one component to help inform the development of an IDB-funded climate resilient coastal management and infrastructure program. And the program involves funding a number of different climate resilient pilot projects, including restoration on several of the islands in the Bahamas. So the key questions that we're driving the work in the Bahamas are where are people at risk from coastal hazards in the Bahamas? And how might sea level rise change the distribution of risk across the country? And then understanding where coastal and marine ecosystems provide protection currently and under future sea level rise for the most socially vulnerable populations. So this is an example of the types of results that are produced by the coastal vulnerability model. So on the left is a map showing the distribution of relative risk across the Bahamas under the current baseline scenario. So in this case, you see three different categories and the red shoreline are those areas where the risk of exposure to coastal hazards is greatest, relative to the country as a whole. And one of the things that the model helps to understand is not only the distribution of risk, but what is driving risk. So in this case, in the gray, you can see the continental shelf, the extent of the continental shelf, and you may notice that areas in the Bahamas that have the greatest potential for exposure to hazards. So those are the red areas are often those areas that are sitting on the side of islands where you have long expanses of continental shelf. So in this case, the potential for the exposure to storm surge is a very important driver underlying the distribution of risk across the country. On the right is a graphic that is summarizing a couple key metrics for different scenarios that were evaluated. So in this case, we summarize the population, so the number of people living along the highest risk shoreline, so the red areas, and also the length of shoreline that is highly exposed. We evaluated this for both a current and a future sea level rise scenarios. And then we looked at a baseline scenario with all coastal and marine ecosystems intact. So that's the white and the gray speckled bar. And then we also looked at another heuristic scenario where coastal and marine habitats are removed entirely. And we assume that this is an extreme scenario that is unlikely to occur. But nevertheless, it's a very effective way of understanding the potential role that habitats are playing currently in providing protection and where. These are similar maps, but they show a little bit more of the detail provided by the model. So in this case, we're zooming in on two islands in particular. We're looking at the island of New Providence on the top. And we're looking at the island of Grand Bahama on the bottom. These two islands are two of the most populated in the country. And New Providence is the island, is the capital island. So Nassau is on New Providence. So on the far left panels, you can see the different coastal and marine ecosystems that are providing protection for these two islands. And then in the center panels, in the red areas, those are areas that are, so the areas in red, the areas in pink, are those areas that are currently at high risk with habitats intact. And then the areas in red are those additional areas along the shoreline that would become high risk if habitat was lost. So as you can see, for both islands, a considerable amount of shoreline would become high risk if you lost the protective benefits afforded by marine and coastal habitats. We are also showing you the distribution of population. So in the blue is the coastal strip of population values with the darker colors representing higher population densities and the lighter colors representing lower population densities. And so you can see that on both islands areas where people are living are also high risk areas, especially on Grand Bahama. And then you can also see that there are a number of different areas that would increase significantly in risk if shorelines, if habitat was lost along the shorelines where people are currently living. This information is also summarized in the bar chart on the far right. So in this case, we're showing the population at highest risk of exposure to coastal hazards for each island under three scenarios. The white bars show the current scenario with habitat intact. The solid black bars show the current scenario if habitats are lost. And then the furthest right bar with the white hatching shows a future sea level rise scenario without habitat present as well. In this case, the percentages refer to the fraction of the total island population at highest risk for each scenario. So for example, on Grand Bahama island, 4% of the population is currently at risk, at highest risk of exposure to coastal hazards with habitat intact. If you lose habitat, even under current sea level rise or no sea level rise, you see 24% of the population becomes highest risk of exposure to coastal hazards. And this increases further to 33% of the island's total population at high risk of exposure with future sea level rise. So some key results from this analysis. Nearly one-fifth of the coastline and nearly two in 10 Bahamians, country-wide, are currently at highest risk of exposure to coastal hazards. If you incorporate sea level rise, the extent of shoreline that's most exposed to coastal hazards would more than double, and the total population would nearly triple. So this would be more than 10% of the population and more than 40,000 people living in highest risk areas. In terms of coastal and near shore ecosystems, they occur along almost the entire coastline of the Bahamas, and there's often multiple habitats fronting sections of shoreline. So this could be coral reef backed by seagrass and then mangrove along the shoreline. And our results suggested that if these habitats are lost, even under current sea levels, the length of shoreline that is most highly exposed to coastal hazards throughout the country would quadruple. Lastly, with habitat loss and modelled sea level rise, the length of shoreline at highest exposure increases fivefold, putting an estimated quarter of the population at highest risk. So this presentation has been designed to give you a sense of the information underlying the coastal vulnerability model and also a sense of how it is typically applied and some of the questions and results that come out of the model. The next thing that I would like to do is to take a quick look at the model inputs, the user interface, and the model outputs. So as you probably know from watching some of the introduction to invest, invest is an open source suite of tools that are modular. So today we'll be working with one specific model, the coastal vulnerability model. Each of these models has sample data associated with the model that can be downloaded as part of the software or separately from the website that are designed to give you a starting place to understand how the model works. You can use, you need to use a GIS. You need to have basic understanding of using a GIS. You do not need to be an expert by any means and you can use ArcGIS or QGIS, which is a free open source GIS software. So in this case, I'm using invest 3.9 and I have downloaded the sample data for the coastal vulnerability model. These sample data are from the Bahamas case study analysis that you saw in the previous presentation and we'll just look at them very quickly. So one thing that you will need to create for every region that you do this analysis is an area of interest. So this is called an AOI and essentially an AOI is just showing the model the area over which you want it to run. So if I pull in my land polygon, I can see that here my AOI for the model is the northwestern corner of Grand Bahama Island. And because the coastal vulnerability is a relative model, the results are going to give you an understanding of the distribution of risk relative to all the other shoreline segments in the model. So other inputs include information about the coastal and near shore habitats. So in this case, we have information about the distribution of mangroves, the distribution of coral reefs, the distribution of seagrass, and the distribution of coastal forest. We also have in addition to shapefiles and rasters, the invest models often utilize CSVs to help give information about model parameters. So there is a CSV that is associated with there is a CSV that is associated with the natural habitats, which gives the model a number of different pieces of information. These include an ID and a pass for each shapefile. They also include the rank associated with each habitat. So as I said, some of the ranks are absolute values that are assigned by the user. In this case, natural habitats fits into that category. And then there is also a protective distance that corresponds to the distance over which the model should look out from the shoreline to find a given habitat. So this is essentially a computational shortcut that helps the model understand which habitat types are providing protection for a given coastal segment. And we have more information in our user's guide to help you understand how to assign both ranks and protective distances. Other inputs are the symmetry, information about the symmetry, information about the continental shelf. So in this case, this is one of the global datasets that's available with the invest model that you can use anywhere in the world. So it's a map of the continental shelf. There is also information about the topography. So in this case, it's the SRTM 30 meter topographic map. And this is what the model is going to use to calculate coastal relief. There is also information about the coastal geomorphology. So in this case, this is often a dataset that is either hard to come by or incomplete and requires some creation effort on behalf of the user. So in this case, the key, I'm going to try sharing my screen again. So in this case, the information embedded in the coastal geomorphology layer that the model is using is going to be information that you put directly into the shape file itself. So in this case, the important column is this rank column, and it's going to tell the model how to rank each shoreline segment. So you can see here, we have different shoreline types. In this case, mud and mud is assigned a rank of four. If we scroll down, we'll see that Sandy Beach is assigned a rank of five, and rocky shorelines are assigned a rank of three. Now, it may be the case that you live in an area with good information about shoreline geomorphology, and there are a lot of different classifications. The model really only needs to distinguish between very broad, categorically different shoreline types. So rather than trying to figure out how to reflect the difference between large gravel beach and medium gravel beach and small gravel beach, really what we want to understand is the difference between rocky shorelines, gravel beaches, muddy shorelines, sandy shorelines. So it's a fairly coarse representation of coastal geomorphology. The WaveWatch 3 dataset, again, another globally available dataset associated with the model. As I said, this is a grid for the world, and each of these shoreline point or each of these grid points has information about wind and wave statistics that the model uses to calculate the wind and wave ranks. And then the last input is the population. So again, this is going to be a raster dataset that's going to give the model an understanding of where people are living. And if we symbolize it slightly differently, it becomes clear where the populated areas are. So those are the inputs associated with the models. And now we will look briefly at the user interface and then we will come back and look at the model outputs to give you a sense of what that looks like. So in terms of the model interface, if you go to your start menu and scroll down to invest, I have a number of different versions of invest, you'll see each of the different models. So invest is modular. And so each of these models has its own user interface and its own modular workspace. And if we click on the coastal vulnerability, it takes a moment to open. In this case, I have some outputs pre-populated in from another analysis I'm working on. So we can clear those or not. All right, it's working just slowly. This is a very typical invest user interface. So it's going to give you a mix of spatial data, rasters, vectors, and then it's also going to ask for some CSVs. And then in some cases, it'll ask directly for some parameters. So there are a number of different ways to navigate to your inputs and input them into invest. You can click on the folder icons or the file icons that are on the right side of each input. And when you do that, it's going to take you to your folder directory and then you can navigate to an area that you want to save your results. So in this case, I might go to my invest folder and save it in the sample outputs folder. When you're working on a specific application, this might say, you know, my region baseline, no sea level rise, and the date or something. So your workspace is where the model is going to send all the results and the intermediates. And you can optionally add a suffix in to help with your organization scheme. So then each of these various inputs hold on. So each of the inputs in this case, we are going to load the area of interest. So we will navigate to our through our Windows Explorer, navigate to the invest folder. And then I can see here that I have the sample data, close to vulnerability. And then I'll be able to see all of the different files that are in my sample data. So in this case, I would load the AOI, that's my area of interest shape file can load my AOI. Everything you'll want everything to be in the same projection. Here we need to specify the model resolution. If you're running your model at over a country wide scale or a large region, I might recommend running it at a kilometer to start. If you are running it for a smaller area, we often run it at 250 meters. Ultimately, your resolution is going to be dictated by the quality of your data, the computational time required to run the model and the size of your region of interest. So for example, to run it one kilometer, I put 1000 meters. To enter in my land polygon, again, I'll navigate to my sample data folder. And I will look for my land mass polygon shape file. So each of the inputs can be input in the same way by navigating to the specific files. If you need information about a given input, you can click the information tab and it's going to point you to some information related to that input. Each of the inputs and the way to create them is described in the user's guide chapter for each model. There's also a way to save and load parameters by creating these JSON files. So in this case, with the sample data, with invest, the download of invest came these JSON files for each of the models. So I could load the coastal zone grand Bahamas invest JSON file and it would go ahead and populate the inputs for me in order to run the model. So I don't have to run each of them myself. You can also create a JSON file once you have loaded your whole, all the inputs for your particular model application. You can save as and you will be able to save your own JSON file so that you can always easily load your inputs without clicking through the interface. In this case, we've tried to make it clear whether or not an input has uploaded correctly by providing a red X if you need to revisit the input and a green check mark that shows if it's been correctly uploaded. Once everything has been produced or loaded into the window, you click run and the model should start running. So in this case, I have already run the model and we'll look briefly at the outputs in the NRGIS. And as you can see, my computer is very slow. I'm sorry about that. Let me put back up the LAN polygon and then we'll look at the outputs. So on the right here you can see this is what will be produced by the CV model. So there's a file here called or a folder called intermediate. In the intermediate are all the different individual inputs and associated information for each one. So for example, for all the for the wind wave, you can see a number of the different steps that the data produced by different steps associated with calculating the wind and wave parameter. The model produces something called a log file. And when you are trying to troubleshoot why you were getting an error or communicating with the invest software team for any reason, you'll want to refer to this log file. So it's going to tell me my inputs. So what I what my inputs were for this particular model run. And then it's going to give me a lot of information, which I don't need to understand. And then ultimately at the end, if you were to get an error, there will be some description here that may help you to understand what caused the error. So even if you have a run that ends with an error, a log file would be created and that is going to be one place that you're going to want to look to see if you can track there. So the outputs, the main coastal exposure outputs are going to be in this coastal exposure geo package. And if I pull in the information, what you will see is shoreline points that correspond to the segment size that you specified in the user interface. And when I open up the attribute table, you'll see there's a lot of different information in the attribute table. So there's a unique identifier, a shore ID associated with each point. And this is going to be very important if you want to do post processing and then join it back to the original table. And then you have the different information, the different ranks for each of the various input variables. So this is our underscore hab. So this is the habitat rank, the wind rank, the wave rank, third rank, relief rank and geomorphology rank. And as I said in the introductory talk, part of what the model is designed to be able to speak to is not only where is risk distributed along your shoreline, but what are the drivers? So understanding the underlying ranks for the input variables are going to be really important in figuring out the different risk drivers. You're also going to have information about the number of people. So in this case, this is where you have null, it is likely that there's no data for that cell, but the values are the average population density within a search radius for a given shoreline point. So as I said previously in the talk, the main output for the model is this exposure index. So this is the continuous variable ranging from one to five. So here we see we have an exposure index associated with each shoreline point. This is a metric which ultimately many times people choose to categorize further. For example, you could convert this or look at the different quartile breakdowns here and consider each quartile to be a risk category. And instead of showing the continuous variable, show something like high, medium and low, for example. The exposure no habitats is a scenario that is automatically run by the model. And this is the scenario in which we assume that all coastal and marine habitats are gone. So this is that without habitat scenario that I was showing you in the Bahamas case study example. And again, this is designed to be a an extreme scenario that is a bookend for understanding the protection provided by coastal and marine habitats. And then the last piece of information is the habitat role. And this is the difference between the exposure no habitats and the exposure index with the habitats intact. So for example, if I were to symbolize my sample data set by a few of these categories, I could symbolize it by coastal exposure. And so in this case, the purple colors, the darker purple colors are going to be areas where you see greater coastal exposure. So again, we're seeing it on this northern shoreline of the island. And my guess is that it would be driven by the fact that this is a slightly more low lying than the other shoreline. And it is also on a wide shallow bank with a high potential for storm surge. It also does not have the benefit of having a fringing coral reef close to shore. So if you'll remember the habitat data, this southern shore has a reef right offshore and the north shore does not have have a similar reef. If I were to instead plot by the habitat role metric, this is going to show us those areas where habitats are playing the greatest role in providing protection. So these are areas where you particularly see the importance for protecting existing habitat is in areas with high habitat role. And then if I wanted to look at each of the underlying driver, for example, maybe I wanted to see the storm surge ranks. In this case, the five again is the greatest contributor to storm surge potential, the greatest contributor to risk in this case, based on the distribution of storm surge. One is the lowest potential for exposure to storm surge. And here we see the importance of that underlying driver. So a much lower potential for storm surge on the southern coast and a much higher potential for storm surge on that northern coast. And again, that is driven by this continental shelf distance. So I'll end this tutorial here. Thank you for joining me. And please do not hesitate to get in touch with us if you have questions. Thanks so much.


ams. They're all extremely valuable. And I think the passion we see around it reflects that. All is a favor. Hi. Any of us. Motion to adjourn. The moved. Second. Second. I was in favor. Thanks everyone. This is great work. It's been a tough budget season. I really appreciate the thoughtfulness.


In a moment you'll hear Robert Young starring in Father Knows Best, but first this listening reminder Tomorrow evening on most NBC radio stations listen to the great comedy shows presented by Phil Harris and Alice Faye and by Bob Hope there's my element fun on the fast-paced Bob Hope show when tomorrow night He trades gags and songs with guest star Bob Crosby Of course less brown and his band of renown will be on hand to furnish the musical backing and you'll be pleasantly entertained by all Concerned so make it a date to hear the Bob Hope show tomorrow evening And then stay tuned for Phil Harris and Alice Faye and another half hour of mirth and music Friday's fun day on NBC radio And now it's Father Knows Best on NBC Now listen to Father Knows Best transcribe starring Robert Young as father Another half hour visit with the folks in the White Frame house on Maple Street sit back and enjoy life with the Anderson Kathy Bud Betty Margaret and Jim has the head of this typical American household again sets out to prove that father knows best Not too many years ago an ingenious inventor by the name of Alexander Graham Bell invented the telephone This is an instrument which makes it possible for two people to talk to each other even though they may be some distance apart It's done with wires Now the telephone turned out to be a pretty popular gadget In fact so many people acquired telephones that there just wasn't wire enough to give everyone a private line So some people have party lines in the town of Springfield the Anderson's have just been placed on a party line And it's creating considerable interest like this Bud what are you doing? I'm listening you ought to hear this conversation Bud Anderson put down that telephone What's the matter you're not supposed to listen to other people's conversations why not they didn't know I was listening Margaret I'm home. We're in the den dear. Oh, hello, honey. Hello, dear. How was your day? Fine? Oh, hello, bud. What are you doing? Just standing here This been a real good day Margaret remember Don Sheridan his wife's the big wheel in the social set Yes, I had a note for Mrs. Sheridan today. Yeah Well, I met him today and he mentioned that his wife wanted you to join some exclusive club that she runs or something That's what she said in the note. I couldn't believe it nice people to know if things go right I just might get the insurance on that automobile agency of his a big fat account Well, there's a saying in town if you're in with the Sheridan's you're in period I think I'll call Pauline Chambers and tell her about that note from Mrs. Sheridan.  What's the matter somebody on our line? Yes The phone company called this morning and said we'd have to be on a party line temporarily till I get some new equipment or something. I Wonder who they are Who you're talking about? I was just wondering who the people are on our line. Well, it doesn't matter Maybe it's a good thing to have somebody else on the line give us a little rest around here In fact the way the kids use this telephone those people whoever they are will be lucky if they get a chance to call the operator This chair feels good That's it I'm a little concerned About what? Well, it seems the new people have been on the line every time I picked up the phone today. Just coincidence I hope we don't have any trouble with them Did you see this little story here on the first page? I haven't looked at the paper story about a couple who got married down in Texas the other day That's so unusual about a couple getting married in Texas. I imagine it happens every day Yeah, but get this the groom was 90 years old and the bride was 87. Oh They're taking a honeymoon trip to Florida on a tandem bicycle. Oh, that's interesting. What does it prove dear? I don't know. I guess it shows that people as they grow older nowadays or younger than they used to be Yes, I suppose so. Well, it's true. Look at us for instance. I don't want to go to Florida on a bicycle What I mean on he is that 40 or 50 years ago people our age considered themselves middle-aged What are you leading up to? Well, it kind of an interesting thing happened today. I was downtown You've seen my red shoes. I can't oh, excuse me father. Go ahead princess. No, you were talking and I interrupted What were you saying? Oh, I was just telling your mother that sort of an interesting thing happened downtown today It's one of those things. That's kind of hard to explain. What happened. Why is it hard to explain? Well, it's odd. I Was in the car driving along and this other car pulled up and sort of drove alongside of me I thought at first it was some friend of ours somebody I knew Then I looked over to see who it was and I didn't recognize her her a woman Yeah, and a quite young and attractive woman and she kept looking over at me and smiling. Oh, no What do you mean? Oh, no An attractive girl smiled at your father Betty now don't spoil it let him live a little After all it's not Impossible I Didn't say it was impossible. It just seems I mean Well Why? Princess I didn't ask her why and I only mentioned it because it seemed kind of interesting and Unusual she probably thought you were someone she knew a friend of her father's maybe Possibly but it seems so odd she could see very plainly. We weren't more than 10 feet apart No, I can understand it dear. You're just young-looking and handsome and irresistible.  Let's leave my age out of this On the other hand though it follows what follows our psychology prof was saying yes Today how you can always tell when a man has reached middle-aged he begins to think young girls are smiling at him Why bet he princess the only reason I mentioned the incident was because by the way mother I'm having dinner over at Janie Liggett's if it's all right. I don't mind if Mrs. Liggett invited you Yes, I'd better call and tell Janie. It's okay That's funny. Is there somebody on our line? Oh, no, they're not still talking They're not talking but the receiver must be off the hook. I can hear a conversation like in a room Well hang up Betty. Don't listen. I wasn't listening. Oh Don't be grumpy father. Just because I told you what I psychology that has nothing to do with it I was gonna stay over at Patty Davis's but I didn't I came home. What happened? Oh Patty's father thinks he's so cute Always dancing around and acting silly. I like my daddy a lot better Well, thank goodness. I have someone on my side.  I like you a lot better daddy.  You're in trouble now princess. Do you mind? I didn't say anything Betty. You'd better come out in the kitchen with me Well, I'll explain it to you All I said was that the point is Betty that your father has reached the age where he's not really old at all But he needs to have someone as sure in that he isn't Well, I'm going over to Janie's. We'll probably go to the library Call us if you want us to pick you up. I will this family trying to keep everyone happy Dear, are you still in the den? Yes, I'm still here. Where'd Kathy go?  Oh fiddle Wasn't gonna say this but I'm positive that mr.. And mrs. Sheridan are inviting us into their crowd just because of you Oh, undoubtedly probably think I'll make a peachy chaperone. Hey dad. What is it bud? What goes with our phone? I've been trying to get Joe for an hour. There's always somebody on a yacking away. Oh dear Who can they be talking to? Yackety yackety yackety. Well, they can't talk forever. Just wait till they hang up I've been waiting and I have calls to make I want to call Pauline and ask her about mrs. Sheridan How to approach her and all I I'll admit I'm a little nervous nervous. Why well, I've never met the Sheridan's We want to make a good impression. Don't worry. We will I know she's been around asking questions about us People have told me how relax Margaret. She'll find out soon enough what kind of people we are I just got to get hold of Joe. Why don't you go over there? No, I told him I'd call him I can't use the phone somebody on their Gavin away. They won't quit. Have you tried the phone here in the breakfast room? It's the same one as in the den. This is just an extension. Are they still talking? Yeah, they're still having women always talking. Why don't you tell them to hang up? No, they wouldn't you know the whole trouble They're on the same line.  Yeah Look, I'll put the phone here on the table so they can hear us talking Well kid, I guess the old man did all right when he knocked over the bank last night Yeah, pops a real good burglar. Hey, they stopped talking. They're listening God, he said I could go with him on his next job. It isn't gonna be much. We're gonna hijack a beer truck. Oh Goodie now we can have beer I got oil up my get pop says we may have to plug a couple of guys Hey, it worked. They hung up boy. You're pretty smart, bud. I wonder who they are I don't know one name. It kind of seems like I heard somewhere Mrs. Sheridan Didn't we? Why we sure did To a father knows best in just a moment There's wonderful radio entertainment all day long when you keep your dial tuned to this same NBC station You'll enjoy such fun-packed programs as the Bob Hope daytime show Tommy Bartlett's welcome travelers Jay Stewart with it pays to be married and a host of others So be sure to keep your dial set to the NBC radio network You'll hear your favorite daytime dramas on NBC to such perennial favorites as the Road of Life Pepper Young's family the right to happiness backstage wife Stella Dallas the woman in my house and young Witte Brown All of these well-liked daytime programs are yours for the listening every weekday Monday through Friday on the NBC radio network Remember what we said about looking into the future It just can't be done and with conditions shaping up as they are in the white frame house on Maple Street Maybe it's a good thing It's bad enough to know what's happened already what with bud and Kathy trying to discourage the people on the Anderson's party line No, we won't even think about what might happen next Let's just go back to Margaret and Jim in the den happily unaware of what transpired in the breakfast room a few moments ago You know you're right dear. Hmm right about what I shouldn't worry about what kind of an impression We'll make on Mrs. Sheridan. Of course not.  I told you they would you just have to be patient That's all it's just that I've heard that Mrs. Sheridan is so well strict and proper about everything Hello Pauline This is Margaret fine Say I have some news. I received an Pauline Something's just come up.   Why would anyone want to listen? I don't know but I heard them lift the receiver Jim there must be some way of finding out who those people are there is ask them Oh, you can't do that. I don't see why not hey mom You should have heard it heard what Kathy and I fix those people on our line.  We scared them out They were gabbing away, so I left the phone off the hook and Kathy and I talked real loud so they could hear us Oh, bud after they heard us. I'll bet they'll never use the phone again. They might even leave town I don't understand it was a slick idea we pretended like dad was a gangster what? Sure scare them. We said a lot of stuff about how you robbed a bank and you and I were gonna hijack a beer truck But you didn't What's wrong with it? You want them to quit hogging the line, don't you? Yes, but now you should have heard him boy. Did they hang up the phone quick? They didn't want to take any chances with tough characters like us But you shouldn't have done that. We don't know who those people are Well, you don't have to worry. They're nobody. We know somebody named Mrs. Sheridan. Well, it doesn't make it Sheridan oh that does it bud. Are you sure? Well, I heard one woman call the other one Mrs. Sheridan Why is it somebody you know somebody? I know but she's she's all this is the end the absolute end Why but Anderson you stood right here in this den when I was talking about Mrs. Sheridan Maybe that's where I heard the name. Oh No, that explains why she was listening in on my conversation After what she heard before. Oh, what am I gonna do Jim? What am I going to do? Just calm down, honey? Don't do anything. I didn't know who she was But you stood right on that very spot and heard me. I wasn't listening Margaret Please now be reasonable You know as well as I do that Mrs. Sheridan a grown-up intelligent woman is not going to be taken into By a couple of kids playing gangsters. No, you're right. She wouldn't She'd know it was just the children acting silly. She'd overheard adults talking that would be different Hello Well, you must have the wrong party Jim who is it? Some girl today when you drive your car alongside mine, and I smile at you and you smile at me That this is the moment I live for I didn't dream of you every night ever since I first see you me I mean well, how do you hang up hang up? My wife says to hang up. I Don't know how you found me, but I'm not the one Jim hang up. I'm sorry miss No, I can hang up Goodbye miss Must be good by my love Goodbye Jim what in the world? How do you like that? That woman I told you about the one that smiled at me this morning Must have found the number somehow. What did she want? Me I guess Jim I just know Mrs. Sheridan was listening in on the line. What did the woman say? Oh my gosh What did she say? Well, she said she dreamed of me every night Called me her doll, baby Well, that's all I can imagine what sort of people Mrs. Sheridan thinks we are by now. Why didn't you explain to her? To Mimi That surely wasn't her name Mimi honey, I didn't have anything to do with this. I can't help it if women tracked me down Jim well, we're finished. That's all this will be all over town. You know how people talk now honey. It's easily explained how Well, this girl just called me up. That's all What could I do? It can't be explained, but you believe me. Don't you honey? She drove her car alongside mine. Can I help it if she? Well you and your fatal fascination. It's not my fault. I can't help it I'll get it. I'd better answer it if this is Mimi. Hello Hello, Mrs. Sheridan Yes, yes, I received your note Yes, I'll be at home. Oh I'd I'd be delighted to see you. All right. Goodbye Well, Mrs. Sheridan she's coming right over Why don't we just gather up the children and leave? Well, what did she say? She has something to speak to me about and it's quite important. I know she heard everything first bud Now you and Mimi I can't face her. That's all I just won't be here Now wait a minute Margaret if she wants to jump at conclusions let her jump We've done nothing wrong. This whole thing is We're in here Betty. I decided not to go to the library after all so I What's the matter? The roof has just fallen in Well, what do you mean? You'd better start packing your things Betty before this is over. We'll probably have to leave town I don't get it. Well princess. It seems your Doddering old father has charmed a certain unknown French lady named Mimi right out of her socks Father you're kidding. He didn't do it intentionally that we know but it's been done This this Mimi called up here the one who smiled at him in the car this morning She didn't yes, she did How she tracked me down.  It isn't every day a man my age turns out to be quite so devastating. Oh Fiddle well if you're so devastating Maybe you'd like to explain how all this happened to Mrs. Sheridan sure I'll explain to her no you won't There's a car stopping out in front there. She is What am I going to say to her? I don't think it's important. I'm much more interested in father in this sudden charm Betty stop it. He's completely unmanageable now Oh Here she is. I suppose there's nothing to do but go and face it. I'll talk to her if you want honey never mind Mrs. Addison yes, I'm Mrs. Sheridan. Won't you come in? Thank you You must think a little odd my calling you and coming over this way, but there's something I feel remiss discuss Yes, I think I understand Won't you come into the living room? Thank you I believe I know why you're here, Mrs. Sheridan. Yes, I thought you would know Well, I suppose it isn't necessary to explain that we somehow came to share the same telephone line No, it isn't necessary to explain But please believe me what you heard it. Well, I'm terribly embarrassed. Did you hear everything that I heard? I either heard it or heard about it But how much did Mr. Anderson get when he knocked over the bank Mrs. Sheridan interesting work hijacking beer trucks Please you know how children are you know that was all made up. Oh, I sort of guessed it was I have children of my own, you know Thank goodness, but Mrs. Anderson or may I call you Margaret? Oh, please do Your husband and I'll admit I listened on the party line Your husband and me me There's something you should know I know all about it, Mrs. Sheridan believe me. It's nothing. That's what you think There's something about me me that you don't know excuse me, Mrs. Sheridan. I'm Mr. Anderson. Well doll, baby It's silly absolutely silly. I I don't know this girl this me me. She's a perfect stranger Oh, no, she isn't she isn't he knows her very well But Who is she? Your daughter Betty No, Betty you see after you hung up Jim I who had been listening to the whole thing talked to me me When she learned who I was she explained what she had done and why Why that? Rascal, so I knew I had to come over here right away because I knew how you must be feeling Margaret. Oh, thank you so much Oh, it's the funniest thing I've ever heard me me Well, that's all I wanted to tell you Margaret. I'll see you at the club meeting Goodbye, Mr. Anderson. It's been a pleasure meeting you doll, baby It's been a pleasure meeting you I think oh by the way We're getting our private phone lines back tomorrow. Goodbye. Goodbye Well, my irresistible charmer. Where is that girl? Betty Betty Anderson's will be back in just a moment Every weekday evening you'll enjoy a visit with one of America's most famous couples Fiber McGee and Molly now every evening Monday through Friday The NBC radio network brings you an amusing adventure at 79 Wispel Vista with Marion and Jim Jordan Portraying their ever-famous roles of Fiber and Molly you'll chuckle with delight at the wonderful situations in which Fiber becomes embroiled Yes, it's truly entertaining listening from beginning to end five nights a week right here on the NBC radio network Make it a date to hear Fiber McGee and Molly every night You'll be glad you did and remember to keep abreast of the latest news of the world Be sure to listen to Morgan Beatty's news reports weekday evenings on NBC radio Well, it's hard to believe but the Anderson's came through the day with no damages outside of Jim's slightly wounded pride He's nursing that injury right now as he sits in the living room with Margaret still trying to explain Well things like that do happen, honey. Oh, Jim. All right, I remember Fred Hathaway some girl called him up He didn't know who she was Turned out he'd given her his seat on a bus two years before a Likely story besides. I'm not the world's ugliest man second ugliest maybe but not the ugliest I Know dear by the way, where's Betty? Oh, she went out somewhere The point I'm trying to make Margaret is that it's not impossible that some young lady might just might find me attractive Sure, sure Probably for me. I'll get it Hello who? Yes, he's here It's for you dear me. Who is it some southern girl named Laura Says she does hi has to talk to you. Oh, what do you know? When you finish dear tell Betty if she's near the grocery to bring home some potatoes Join us again next week when we'll be back with father knows best starring Robert young as Jim Anderson father knows best as an NBC radio network Production in cooperation with Cavalier Enterprises in our cast for Rhoda Williams is Betty gene Vanderpile Ted Donaldson Helen Strome and Virginia Gray father knows best based on characters created by Ed James is written by Paul West and Roswell Rogers directed by Arthur Jacobson and transcribed as Hollywood. This is Bill Foreman speaking Tonight play truth or consequences on the NBC radio network


Hello everyone once again I welcome you all to MSB lecture series on transfer metal chemistry. In my previous lecture I was discussing about metal-metal multiple bonding while describing the reason for metal-metal multiple bonding I also showed you how one can use effectively molecular orbital concept to understand the significance of metal-metal bond and also to understand the magnitude of the metal-metal bond. Let us continue from where I had stopped. Today we shall look into more quadruple bond and quintuple bonding we come across among coordination compounds. I have given here two examples look into it carefully one is a chromium complex and another one is also a chromium complex but ligands are very different and you can see in the first one you can see orthometallation is there on opposite side and you see weak interaction with this phenyl group carbon atom. So you are having some sort of interaction here and same thing is true here so that means essentially chromium has one ligand that is a very bulky ligand and one should remember if you want to stabilize any metal in its low valent state which is coordinatively saturated only ways you have to do it kinetically how to do it you have to go for a very bulky ligand so that it will give umbrella protection surrounding the metal atom so that it does not give scope for other bigger ligands to enter. So in this context initially this mono-ligated chromium compound was made and then this interacts to such mono-coordinated chromium species they interact with this ortho substituted carbon here you can see and as a result what happens it appears to be having two coordination with almost linear geometry and in this case what if you carefully count here we have five bonds are there here and if five bonds are there how to explain that one same thing is true in this case also whereas here nitrogen is there central pyridine nitrogen is coordinating and also the nitrogen lone pair is coming here see this is the ligand used in case of the first one and in the second case we have used this ligand here and you can see here first it binds here and you will be having almost one is to one metal to ligand ratio and then since this side is open what happens two such molecules will arrange in such a way that something like this they come and they establish a dive metallic core. So now we have to explain the origin of five bonds in this one how to do that one again we have to go to the same concept that we used earlier to explain multiple bonding like single bond, double bond, triple bond or up to four bonds or quadruple bond same analogy we should use here one interesting thing is here in previous case we were just confined to four orbitals that is dz square something like for me sigma bond and then we had dx z and dy z forming two pi bonds and then we had dx y forming delta bond four orbits we used. So four orbital means if the capacity is 8 then you can have bond order of 4 but if we want to have bond order of 5 in order to see the formation of quintuple bond we need 5 electron on each metal d orbitals that means d5 configuration we had to take in but in earlier case when we took d5 what happened was 2 electrons were going to delta star delta star as a result what happens bond order was reducing from 4 to 3 but in this case what happens since it is using sp hybridization we are left with all the 5 d orbitals for bonding purpose. In square planar complexes we are using dx square minus y square for making method to ligand bond you just recall VLS bond theory where we are using dsp to hybridization that d orbital is dx square minus y square whereas in this case it undergoes sp hybridization to accommodate to 2 ligands surrounding chromium autumn as a result what happens all d orbitals are left unutilized and chromium electronic configuration if you see d4 s2 or d5 s1 and this ligand if you see the top one if it is mono anionic here through CH activation it forms so then it is a mono anionic means at least it should be plus 1 and as a result what happens chromium will be left with 5 electrons so 5 electrons are there and 5 d orbitals are there. Now that means along with dxy dx square minus y square also participates in delta bond formation so that means now we have 1 sigma bond through dz square and 2 pi bonds are there through dx and dyz and then we have overlapping of dxy and dx minus y square that leads to 2 delta bonds that means 10 electrons are there in the bonding 10 divided by 2 equals 5 so bond order is 5 this is how you can visualize printable bonding in this kind of compound so that will become clear when we go to the next one. So again just I have shown here how it happens so next you can see here yeah this is a compound another compound here of course here you can see just go back to this one and here if the delocalization happens here of course this is anionic you should recall here n 2 are coordinated so n minus this is so here almost is similar to n h 2 minus I would say n r 2 minus so now what happens this is delocalized as a result becomes mono anionic very similar to couple of ligates I showed you in my earlier class in couple of examples showed you in my earlier lecture you can see now anionic it is so chromium or molybdenum they are in plus 1 state so d 5 electronic configuration and how this compound is made I have given the method here first you take this compound where chromium is in plus 2 state CrCl2 coordinated to 2 neutral ligates such as THF you take this one and treat this one with lithiated this aromatic group and once you lithiated this one is there LICL comes out and you left with one CL here as a result what happens it forms a dimeric structure having 2 chlorobidges here and now this one needs reduction for that one Kc8 potassium over graphite is taken slight excess in THF then it will abstract to chlorides to generate chromium one species from chromium 2 and then now chromium one species is co-ordinatively unsaturated it interacts with this carbon here and then it establishes a like a bidented made of coordination having this kind of structure and you should remember that here we have 5 electrons are there in the d orbit so this was reported in 2014 in Dalton transaction so another compound is there I will give you the preparation of this compound also before I proceed to show the amort diagram to confirm the presence of 5 bonds so in this one to take it is already you know quadruple bonded compound very similar to rhenium dimer Re2Cl82- so just look into this one so this is a potassium salt because tetra anionic so what one should do is add a ligand that ligand I will show you what it is and then you can make a compound of this type here I will show you what is A and then again you use the reduction process using potassium over graphite that leads to the formation of a quadruple bonded compound like this so you may be wondering what is A so A is this one here lithiated this bulky aromatic compound here this is the lithiated one and then if you just see here two lithiums here two lithiums are there that means when you are using two equivalent one lithium is coming out as LICL so you can see the missing of one chlorine here and then another one another one will remain here coordinated to lone pairs of chlorine here and then of course once when you reduce with Kc8 everything is gone and you simply ended up with a malbeden plus one the compound having this mono anionic ligands bridging in this fashion I am sure it is clear about repetitive methods so this came in 2009 in JAGS so now let us see here malbeden means plus one state d5 system to form this complex as I mentioned earlier malbeden has not used dx minus y square otherwise it would have used if it had squared by the geometry so now this is also available along with dx y you know oriented in this fashion something like this one is like this and another one is at 45 degrees both of them can only overlap in this fashion when they stacked in along z axis you can see here 5 are there now just see I have added dx minus y square also for this bonding scheme here we have total 5 electrons whether it a chromium or malbeden first to establish a sigma bond by utilizing 2 electrons and now pi bond we are using these 2 these 2 4 electrons so 2 pi bonds are there and now we are using now 2 electrons here one each from dx y and dx y square from both the metal centers and then we establish a 2 delta bond so then the bond order is 5 so this is how you can explain quintuple bonding among coordination compounds having d5 electronic configuration with dx square minus y square available for metal metal bonding. Now let us go to another interesting compound here if you recall my previous lecture where I classified metal metal bonding compounds into covalent bonding, dative bonding and symmetric metal metal bonding so now let us look into dative bond scheme so when a metal with at least 2 d electrons and a moderately high electron count is adjacent to a metal that is coordinatively unsaturated and electron deficient in that case what happens both of them can have some sort of Lewis acid base interaction the one metal that is electron rich can give a pair of electron to the one which is electron deficient provided it has empty orbit of suitable symmetry to take those electrons in that case what happens you can establish a dative metal metal bond or you can call it as a adduct between 2 metal centers so I have given one ideal example here and it is very interesting to analyze this one and there are several ways one can analyze this compound this is a phosphide ligand here you can see pr 2 minus and it is also pr 2 minus and we have 3 carbon groups all together and if you just look into this one well of course this can be plus 2 state nickel can be plus 2 state provided it anionic establishes a bond with nickel in that case what happens this will be plus 2 state that means d8 system d8 plus 4 12 and plus 14 so this becomes a 14 electron species on the other hand this one is nickel 0 now pr 2 acting as a neutral ligand towards this nickel so d10 plus 8 is 18 electron system that means it is very similar to NiCO4 or NiPPH3 4 times now so this is 18 and this is 14 so this electrons can go here so that it becomes a 16 electron species and to an extent electron deficiency can be removed here so you can see how dative bond is directed from this co-ordinatively saturated and electronically rich nickel to this 14 electron species that means what are the other alternates for example we can have phosphide one each on these two metals in that case what happens both of them will be nickel plus 1 state if they are in nickel plus 1 state what happens the d9 system they become both of them become d9 system and then we have anionic on both sides so two electrons are coming here and bridging one neutral also two electrons are coming whereas in this case one carbon monoxide is there and of course there is one metal metal bond you can think of d9 system so 16 electrons will be there for this one and then here in plus 1 state and then you have 4 electron this will be 80 electron but here if it is 16 many d8 systems are stable with 16 electron species you cannot think of a dative bond so since it is an example that is proved through experiments and also x-ray structure so this kind of analysis is incorrect so nickel plus 1 is not correct instead what I said earlier so this is in 0 state and this is in plus 2 state is the correct analysis and nickel with 18 electron is donating 2 electrons to this one so let us analyze for better understanding so now you can see here 2 options so now I have shown both the terms here so I have shown here nickel this is binding to 4 neutral ligands with 18 electrons with d10 system that is 0 and now this is going this way that means now this is in nickel plus 2 because here we are talking about phosphate anionic so this is a covalent bond and this is a coordinate bond now this is in nickel plus 2 and 60 electron species and now this will be nickel 0 so this also becomes 10 plus 60 electron species both are 60 electron species in that case what happens none of them can give electrons to this on the other hand other alternative is as I mentioned nickel 1 nickel 1 can be there again we overruled from my previous discussion and argument and then what would happen here 17 and 18 and now it is 16 and 18 but when we looked into the x-ray data what it shows is nickel 2 NiCO 170 picometer and Ni2 phosphorus 216 picometer now let us look into the bond parameter for this 1 nickel 0 NiCO 178 picometer that means it is longer why it is longer because it is taking some back bonding is there because this electron is rich when the back bonding is more to CO what happened NiCO bond shrinks and CO bond distance increases so here it is electron deficient it is not readily donate electrons to pi star of CO as a result what happens NiC bond is little longer as a result CO bond still has triple bond character so it is slightly shorter so that means this proves that this is in plus 2 state and this is in 0 valent state and again that is also reflected from NiP 224 picometer this is a coordinate bond whereas here we have a covalent bond because it is an anionic P minus so 216 this is shorter always covalent bonds are shorter compared to coordinate bond so this is another proof to say that this has a dative bond with nickel 0 18 electron species and nickel 2 14 electron species so this proves beyond any doubt that there is a dative bond between 2 nickels and this 0 valent nickel is donating a pair of electrons to this one to overcome its electron deficiency this is how one should analyze carefully by looking into all parameters and NINI distance is 241 picometer or 2.41 anion strength units so now let us come to the last one I did mention about symmetry metamethyl bonding so what would happen here is weak metamethyl interactions observed due to the molecular orbital symmetry interactions of field and the empty metamethyl bonding orbitals under anti-bonding orbitals observed in D8 system and is very rare D8 system should not have any metamethyl bonding due to the zero bond energy as both bonding and anti-bonding orbitals are completely filled that means you can see here for example if you recall a more diagram just I am writing here 2 electron sigma 2 pi here and 2 delta and then again delta is filled and pi is filled and then sigma is filled so this is sigma 2 pi and then delta delta star pi star 2 pi star and sigma star so all 10 electrons are there so bond order is 0 so that means you do not expect any metamethyl bonding and in a square panner complex having D8 system but HP gray and others observed in a few bi or polymetallic complexes having D8 electronic configuration and showed the presence of weak metamethyl interactions both in solution and in solid state that means if you observe in solution these molecules retaining weak interactions and stacking along the axial position probably there is bonding that has to be explained so one such complex of iridium with isocyanate showed weak metamethyl interactions and that gave enough proof to go further you can see here this iridium complex here iridium plus compound D8 system having neutral isocyanate ligands are stacked up in this fashion having unusually shorter iridium iridium bond despite war rule we war rule any metamethyl bonding as bond order comes to zero then how to explain this one so gray proposed in 1974 that these weak metamethyl interactions were caused by a molecular orbital symmetry interaction between the field sigma metal metal bonding and sigma star anti-bonding orbitals with the mt pz sigma and sigma star orbitals you should remember when we think of valence bond theory to explain square planar complex geometries we are using dsp2 that means d orbital is dx my square and then we are using s orbital along with px and py orbitals and pz orbital is left unutilizable in a square planar complex along with four other orbitals but in the d8 what happens everything is filled that means now the interaction between dz square where we have sigma sigma star field and unfilled they interact with mt sigma sigma star of pz orbitals that means take pz orbitals from two metal centers and try to establish a molecular orbital sigma pz and sigma star pz and then they are empty and whereas this is filled that means there is some interaction happens between these two pair of molecular orbitals the field dz square bonding and anti-bonding orbitals with mt pz due to this kind of interactions the mt orbitals are pushed up in energy and the field orbitals pushed down in energy by this symmetry interaction so due to this kind of arrangement this generates a weak metamethyl bond strong enough to allow these complexes to form mm bonds and retain them even in solution so this was observed in many complexes of iridium and even palladium and platinum compounds with the d8 electronic configuration probably if you look into literature and work by gray you will come across more examples with this I am completing the discussion on metamethyl multiple bonding so once this is done you should remember one thing when we predict metamethyl bonding in these cases where you can have single bond double bond triple bond quadruple bar or two quintuple bonding the concept usually is very different compared to what we come across in predicting metamethyl bondings while using 18 electron rule sometime they may be in some cases it does not apply do not try to mix up their different entities and different type of components we come across these things maybe there may be some examples where we can see both merging to give a correct explanation about 18 electron rule nevertheless their entities but beautiful concepts and without any problem one can explain using simple molecular orbital concept what one should remember is again in case of square planar complexes we have four d orbitals are there d z square d x y d x z and d y z and d x z and d y z generate two pi bonds d z square generates one sigma bond and because of their orientation d x y will overlap only like this only like this and they generate delta bonding and then we have eight electrons in the bonding and if bond order is nothing but number of electrons present in bonding orbits minus number of electrons present in anti-bond divided by 2 so that gives the bond magnitude of 4 to have four bonds or quadruple bond though a square planar metal complex should have d 4 electronic configuration or they should have four electrons in their d orbitals and if we have unutilized d x y square also with lower symmetry or sp hybridization for example in that case what happens d x square minus y square is also made available for bonding in that case all 5 d orbits are available for bonding you can imagine or you can explain the quintuple bonding there may be more examples of course if you are more interested you can always go to literature and look into more examples in my next lecture I shall discuss about the preparation of some of the important coordination compounds and argumentary compounds keeping their utility in further reactions and then making ideal compounds for homogeneous catalysis until then have an excellent time reading chemistry.