Metal ion vs. log βMY values.
\r\n\tWe must not forget that farming requires quite different approaches and animal feeding conditions in different farming systems. It is not the same having a larger number of animals or small herds just for family needs.
\r\n\r\n\tHowever, one thing should be common and most important: animal “well-being”, knowledge of the health status of animals, continuous monitoring and, if necessary, healing and treatment of animals. Without this knowledge, infectious diseases, feed-borne diseases and resistance to individual drugs cannot be traced, which can lead to very serious problems and, in some cases, tragedies.
",isbn:"978-1-78984-709-3",printIsbn:"978-1-78984-708-6",pdfIsbn:"978-1-78985-193-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"7e5d45badb49806d949ad1475e3a0ef0",bookSignature:"Prof. Sándor Kukovics",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9706.jpg",keywords:"Goats and Environment, Stress Factors, Goats Feed, Goats and Society, Economy, Meat, Skin, Fibre, Milk, Human Health Benefits, Goat Health, Goat Management",numberOfDownloads:13,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 2nd 2020",dateEndSecondStepPublish:"December 3rd 2020",dateEndThirdStepPublish:"February 1st 2021",dateEndFourthStepPublish:"April 22nd 2021",dateEndFifthStepPublish:"June 21st 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Kukovics is the President of the Hungarian Sheep and Goat Dairying Public Utility Association. He has been the Executive Manager of Sheep and Goat Products’ Board and Inter-professional Organisation since 2010. Between 2015 and 2019 he served as Vice President of the EU COPA-COGECA Working Party on Sheep and Goats and he has been a member of the Board of Directors of the International Goat Association since 2016.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"25894",title:"Prof.",name:"Sándor",middleName:null,surname:"Kukovics",slug:"sandor-kukovics",fullName:"Sándor Kukovics",profilePictureURL:"https://mts.intechopen.com/storage/users/25894/images/system/25894.jpeg",biography:"Prof. Dr. Sándor Kukovics spent 40 years at the Research Institute\nfor Animal Breeding and Nutrition (Herceghalom, Hungary)\nbeing responsible for the small ruminants sector. He also edited\n30 books, published more than 1,000 articles and has licences\nfor 4 products. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"73063",title:"Remote Management of Autonomous Factory",doi:"10.5772/intechopen.93527",slug:"remote-management-of-autonomous-factory",body:'The smart manufacturing initiative from the industry transformation perspective is rapidly emerging in many parts of the world. Few notable smart manufacturing transformations leading the globe are the Chinese version of Made in China 2025, the German version of Industry 4.0, the United States version of Industrial Internet (Made in America), the Singaporean Smart Nation, and the UK/EU version of Future of Factory. Clearly, the global trend is pivoting toward the new era from the traditional mass (“Ford’s”) factory production, where a consumer-oriented low-volume, high-mixed demand and supply production would be the smarter way to build and make things [1].
In the factory, the manufacturing system is illustrated by the pyramid shown in Figure 1. At present, the production shop floor is already highly reconfigurable in its process level. This is possible, starting from the enterprise resource planning (ERP) layer, where process planning, operational management, etc. are interlinked. The manufacturing execution system (MES) layer is set to handle scheduling, dispatching production, etc. in a systematic manner. Monitoring, supervision, and control layer takes care of the real-time optimization and the advanced process controls. Sensing and actuation layer comprises the sensor readings’ collection and manipulation of the production process. The final layer is where the actual production process exists.
The manufacturing pyramid—an overview of the existing production flow [2].
In the above manufacturing pyramid diagram, few layers are already interconnected, but there are some layers which are independent silos. This is the pain point that is addressed through smart manufacturing, that is, introduction of the smart connectivity and interoperability throughout the layers. Various aspects need to be considered as it could be impossible to replace all the hardware in a manufacturing company to enable this. There could be some legacy systems as well which in itself would not be ideal to use in such an environment. By introducing the right cyber-physical tools and network connectivity, this can be achieved.
In today’s mass production era, the world is making things (products and systems) so quickly and systematically in huge volume. The demand for these products is very high and, at the same time, consumers are still in search for a need for making the production very personalized. Hence, the “one mold fits all” approach may not seem to be enough. The present approach is facing the lack of networking between the automation pyramid levels, that is, especially between ERP and MES layers and, in turn, communicating directly with the lower layers is not possible. This missing communication among the process equipment like machineries and field control systems like PLCs at the production shop floors implies that customization at the product layer for the consumer is still in progress in classical manufacturing [3].
Recently, there is a strong pull to shift from the classical manufacturing/automation pyramid to the horizontal and vertical networking and to achieve the cyber-physical production systems (CPPS) as illustrated in Figure 2.
The new paradigm of the manufacturing pyramid—an overview of the changed production flow.
The hierarchy arrangement of the automation pyramid is helpful for tracking the production status and for keeping proper process flow. Chaining the horizontal levels together adds value to the coordination and the vertical levels networks for more interconnection and cooperation. The full potential is attained when the boundaries are open. To reach this CPPS goal, one has to first identify and determine the associated pain points in order to enable the smooth transition:
Vast proprietary silos platforms/systems out there in the industrial systems.
Proper utilization of the available data.
Demand for higher response in terms of demand and supply of products in between the production line and the supply chain flow.
The CPPS is the backbone to the digital manufacturing era. The transition process from classical to digital manufacturing transformation requires a holistic platform of many Mini-MESs as shown in Figure 3. Mini-MES is a new concept being introduced by the authors to solve the existing techniques reported in the literature and is followed by industry best practices. The novel mini-MES platform provides an avenue for the technology process level (the most bottom layer) to interplay interconnectivity and interoperability with its higher levels until the above pain points are addressed holistically. There are four main aspects to be looked at and addressed, namely product, production, process management, and service manufacturing. This platform works hand in hand with the two upper layers to form the full CPPS as mentioned before in achieving the autonomous production factory. The rest of the chapter is going to focus mainly on the factory production of digital manufacturing and on describing the 3-Cs implementation plan, the enabling technology, and the achievable outcome ahead.
Paradigm shift from classical automation toward interconnected smart systems [4].
The 3-Cs implementation plans can be divided into three main parts. First is connecting to enable remote monitoring and management [5]. To truly remotely manage the factory in real time, 5G technology has to be used since the specifications of 5G enable us to achieve a near real-time operation. The desired outcome is to achieve the large-scale deployment of remote autonomous control systems that are to be operated and managed seamlessly.
Second is computing at the edge. To implement as an aggregation point of the factory that holistically connects the dots of the individual levels of the automation pyramid and seamlessly control the communication protocols of devices and equipment of Siemens, Schneider Electric, ABB, etc. with a single common protocol. By doing so, any data point on the factory shop floor is available to the A.I. computation to develop the desired models and throughputs.
Third is collaborating/codeveloping with the domain experts. To leverage on the strong client records of experiences and add on to the implementation of the data platform to drive the traditional manufacturing market in a much faster and penetrable manner. Through the codevelopment process, the digital transformation of factory automation is going to accelerate tremendously while working closely back and forth with existing clients and leveraging on the existing manufacturing solution and the domain expertise.
As you may already realize, the global manufacturing trend is changing from the passive and reliant approach to a more demanding and intelligent way to manufacture produces. That is the smart factory (SF), which is rapidly growing and will be an anchor component of the digital manufacturing transformation journey. According to a financial report, it is expected that the SF market will continue to grow at a compound annual growth rate (CAGR) of 9.6% to reach around US$244.8 billion by 2024. In the case of the autonomous factory, it is projected to be one of the key enablers of these SFs for having the ability to be remotely operated and managed in the autonomous manner. Thanks to the data-driven platforms that are booming in from the digital space, the factory production can turn toward full automation and autonomy. It is expected to transform the traditional factory owners that produce in mass quantities with lots of manual workers into low-volume, high-mixture production with digital manufacturing technique.
The transformation will involve a set of digital technologies, including the Internet of Things (IoT), robotic and artificial intelligence (AI), into the implementation/upgrade for the factory modernization. With this, the new world of making things so agile in a flexible way, optimizing and integrating the system approach, and the great speed and accuracy are going to benefit and satisfy the fast consuming and highly personalized needs of the people (Figure 4).
Autonomous factory flow.
Remote monitoring is a key aspect for an autonomous factory. For a manufacturing company to make money, it ultimately comes down to the number of products made. So, if there is an unscheduled downtime, the throughput of the production line reduces, and hence the company ends up making fewer products and thus loses money. We can overcome this by introducing a remote management solution. This would consist of a list of tools such as remote production monitoring, remote safety monitoring, machine learning, and artificial intelligence for predictive maintenance, which would address the problem even before they occur. As the solution is deployed, the owner will be able to see a reduction in operational costs and an increase in the equipment efficiency.
Remote management and monitoring had been available as a technology for the past decade or so, but somehow, the breakout year was never seen by the experts and observers. However, the situation is poised to change in today’s scenario, with the new economic trends, transformed man power needs, and the availability of new technologies. During the global financial crisis, a lot of companies turned to lean staffing models. There is a certain lack of design and maintenance talents in many companies because of this. Coupling together with this is the retirement of the baby boomer workforce [6], who are extremely hardworking, which eventually led to a change in the demography of a factory floor. The new staff severely lack the decades of hands-on experience on the aging legacy equipment. They are also put under tremendous pressure to deliver a better throughput and more uptime and hence forcing the companies to remote monitoring and management.
Another challenge we all know well is that the present manufacturing ecosystem is highly dependent on China, where most of the factories are located. China has a very strong fire wall, since they need to ensure their cyberspace is well protected, which is understandable. There is a great need for an IT platform that is equipped with the ability to capture true value creation from opening up the interrelationship between the nations like Singapore and China. The data exchange channel link has to be interlinked with minimum latency to empower an autonomous factory to interact on a real-time basis with its clients throughout day and night for 365 days without fail and to report machine health, process flow rate, and production status to the health diagnostic center for close and constant tracking outside China. Take for an example, there is this possibility with the use of the newly built Singapore and China data connectivity infrastructure of less than 60 ms latency between countries to enable online live communicating and producing of productions. This powerful data transmission exchange infrastructure will definitely enable an autonomous factory to work in a well-protected network environment.
The way things are bought and sold is going to change. It will go, way beyond buying from ecommerce platforms to making the products directly for the consumers. Take for an example, a production factory based somewhere in the middle part of China, in a city called Henan province, where there are a large varieties of machining/processing tools on the actual manufacturing shop floor. The digital manufacturing technology made by Printed Power (PP) Pvt. Ltd. (a company based in Singapore focusing on IoT and smart manufacturing) for the plant is an IoT-cloud platform that allows the remote manning, from the other parts of China or even from Singapore, of the Henan’s factory production and maintenance as well as interconnecting the local resource planning ERP with the manufacturing execution system. The power of mini-MES (as illustrated in Figure 3) autonomous factory platform is to do away with: (1) restricted remote connectivity for the manufacturing solution to provide access to their local factory floor within China and (2) limited 24 × 7 real-time domain experts’ monitoring and resource planning and managing of the factory production system.
In short, this proposed mini-MES autonomous factory platform is one of the first types of deployment in the world that compasses IoT-cloud cum edge computing technology to upskill the local manufacturing solution providers in serving their end manufacturing factory clients more effectively and efficiently. The autonomous factory software platform is performing three key functions: (1) to allow the manufacturing solution provider/system integrator to upgrade the traditional factory to be remotely access and control and (2) to support the manufacturing process/operation with the at-the-edge computers to do data compression for fast speed production and for onsite decision and execution for real-time factory throughput.
In this section, we will discuss the key technology enablers of an autonomous factory system. The discussion is going to surround on the digitalization tools, namely, the Internet of Things (IoT) hardware, the AI machine learning software, and the application design thinking/innovation needs. Figure 5 illustrates an overview of the various technology enabling modules that apply to the autonomous factory context.
Technology enablers (reference to https://volansys.com/product-engineering-services/).
Among the many, the middleware or the firmware and operating system layers are the most important for the distributed edge computer network with AI processing at the cloud edge. As so, the edge computer becomes a crucial technological enabler to the transformation of mass manufacturing into low-volume, high-mixed production. The discussion will focus on the product and system of edge computing and its underlying features as well as its association with manufacturing application and solution. As a reference, some of the tools used by Printed Power will be discussed.
Printed Power has developed an IoT edge gateway product as shown in Figure 6, which is a powerful hardware platform with multiple protocol support for wired and wireless communication systems. Edge gateway product supports Android, Linux, and Ubuntu operating systems out of the box and can run a multitude of “apps” for each platform. It is a compact industrial PC combined with a powerful IoT gateway.
Printed power PowerEdge™ hardware specification.
The PowerEdge™ product consists of RK3399 SOC, wireless radios, power modules, multiple GPIO and USB ports, and camera ports enclosed in a weather-proof enclosure with the following:
Light-weight operating system (OS): quick booting time and selected services running to reduce the dynamic memory consumption. The OS is based on the trimmed-down version of Android, Linux, or Ubuntu.
Embedded device driver development: able to support third party IoT devices interfaced over wired or wireless connectivity. Device drivers are dependent on the current OS running on the platform.
Embedded hardware development: able to install add-on hardware for the edge gateway to support IoT devices which operate at different power levels or nonstandard protocol.
OTA update support: allow updatable library packages inside the edge gateway for easier firmware upgrade which is completely wireless based.
On top of that, the Printed Power edge-cloud platform depicted in Figure 7 allows the edge devices that are distributed throughout the factory shop floor and interconnected with the various machineries to gather at one place for monitoring and controlling. The cloud runs machine learning algorithm, makes java class image for download into the edge devices, and interoperates between the cyber cloud and the physical factory seamlessly.
Printed Power edge-cloud platform. (i) Description of platform components. (ii) Application of edge-cloud.
Let us first take a look at what a manufacturing production floor looks like and the kind of stuffs they do. A factory, manufacturing plant, or a production plant is an industrial site usually consisting of buildings and machinery, or more commonly a complex having several buildings, where workers manufacture goods or operate machines processing one product into another [4]. The area in a manufacturing facility is where the assembly or production is carried out either by an automated system or by workers or by a combination of both. The shop floor may include equipment, inventory, and storage areas. The basis of manufacturing [7] can be described in the following areas:
Assembly line: defines the progression and synchronization of work. The Ford example is typical of manufacturing, where there is a specific sequence of events which must be followed in order to produce the desired work product in a timely manner.
Division of labor: breaks the production process into separate tasks performed by specialists or craftsmen. Subdividing the process down into smaller increments provides the means to employ common workers as opposed to developing a dependence on highly skilled craftsmen which may add to the cost to the work product.
Precision tooling: provides mechanical leverage in the assembly line, the need for using the most technologically advanced tools, something that requires constant monitoring and upgrading.
Machines and the people are both working together in the shop floor and they operate with each other simultaneously. The machines are controlled by PLC logic and humans are controlled by themselves or by the company process. It is thus important that the two systems of their own talk with each other.Figure 8(i) is a snapshot of how this has being designed for an autonomous factory and it illustrates the architecture of such a platform design.
Autonomous factory platform. (i) Printed Power digital manufacturing architecture. (ii) Robotic automation system.
Referring to Figure 8(ii), the robotic automation system of the autonomous factory consists of the arm performing pick and place, the moving robot to convey materials and products inside the factory, and the pressing and heating machine to treat and mold the made products. In order for the machines to communicate by themselves within the horizontal level of the manufacturing pyramid as discussed before, one has to first make a common communication language translator that allows cross talk among the machine systems. Printed Power PowerEdge™ fits in to provide the hardware connectivity as well as the firmware language translation.
Next, some form of intelligence is gathered from the data sets collected from the operations and maintenance to derive with the AI learning models for the control and automating of the manufacturing process. The AI learning is supported by both the online cloud-based engine as well as the offline edge Printed Power PowerEdge™ executable controls. GPU is part of this ball game to power up the AI engine and speed up the entire manufacturing process.
Lastly, the remote manning and controlling of the entire autonomous factory platform is achieved via the dashboard at the command and control center. With the manufacturing and production status, the business owner is able to make a holistic and accurate decision with the sales figures, logistic and supply chain availability, demand and supply status, etc. Hence, this is a crucial part of the manufacturing application and solution.
The entire platform is undergoing development and installation at the site. It is expected to deliver its desired outcomes of being able to remotely access and control via the designed mini-MES platform with its backend housed on the AWS cloud. The autonomous factory should operate at its highest efficiency with the installed PowerEdge™ edge data compression for fast speed production and for onsite decision and execution for the real-time factory throughput.
Smart manufacturing initiative or movement from the industry transformation perspective is rapidly emerging in many parts of the world. Today, mass production is making things (products and systems) in a gigantic way that everyone is consuming them not at their preferences. The made from the “one mold fits all” approach does not seem to work for the consumers. The new world of making things is going to be so agile, so optimized and integrated, and so fast in speed and precise in accuracy to benefit and satisfy the fast consuming and highly personalized needs of the people.
To address this, the chapter discusses the digital manufacturing technology made by a Singaporean company, Printed Power, for the plant to be remotely manned and controlled by a press of a button. This autonomous factory software platform which is being developed allows for: (1) the remote connectivity for the manufacturing solution to access their local factory floor within China and (2) the 24 × 7 real-time outside China monitoring and managing of the factory production system.
Stability constant of the formation of metal complexes is used to measure interaction strength of reagents. From this process, metal ion and ligand interaction formed the two types of metal complexes; one is supramolecular complexes known as host-guest complexes [1] and the other is anion-containing complexes. In the solution it provides and calculates the required information about the concentration of metal complexes.
Solubility, light, absorption conductance, partitioning behavior, conductance, and chemical reactivity are the complex characteristics which are different from their components. It is determined by various numerical and graphical methods which calculate the equilibrium constants. This is based on or related to a quantity, and this is called the complex formation function.
During the displacement process at the time of metal complex formation, some ions disappear and form a bonding between metal ions and ligands. It may be considered due to displacement of a proton from a ligand species or ions or molecules causing a drop in the pH values of the solution [2]. Irving and Rossotti developed a technique for the calculation of stability constant, and it is called potentiometric technique.
To determine the stability constant, Bjerrum has used a very simple method, and that is metal salt solubility method. For the studies of a larger different variety of polycarboxylic acid-, oxime-, phenol-containing metal complexes, Martel and Calvin used the potentiometric technique for calculating the stability constant. Those ligands [3, 4] which are uncharged are also examined, and their stability constant calculations are determined by the limitations inherent in the ligand solubility method. The limitations of the metal salt solubility method and the result of solubility methods are compared with this. M-L, MLM, and (M3) L are some types of examples of metal-ligand bonding. One thing is common, and that is these entire types metal complexes all have one ligand.
The solubility method can only usefully be applied to studies of such complexes, and it is best applied for ML; in such types of system, only ML is formed. Jacqueline Gonzalez and his co-worker propose to explore the coordination chemistry of calcium complexes. Jacqueline and et al. followed this technique for evaluate the as partial model of the manganese-calcium cluster and spectrophotometric studies of metal complexes, i.e., they were carried calcium(II)-1,4-butanediamine in acetonitrile and calcium(II)-1,2-ethylendiamine, calcium(II)-1,3-propanediamine by them.
Spectrophotometric programming of HypSpec and received data allows the determination of the formation of solubility constants. The logarithmic values, log β110 = 5.25 for calcium(II)-1,3-propanediamine, log β110 = 4.072 for calcium(II)-1,4-butanediamine, and log β110 = 4.69 for calcium(II)-1,2-ethylendiamine, are obtained for the formation constants [5]. The structure of Cimetidine and histamine H2-receptor is a chelating agent. Syed Ahmad Tirmizi has examined Ni(II) cimetidine complex spectrophotometrically and found an absorption peak maximum of 622 nm with respect to different temperatures.
Syed Ahmad Tirmizi have been used to taken 1:2 ratio of metal and cimetidine compound for the formation of metal complex and this satisfied by molar ratio data. The data, 1.40–2.4 × 108, was calculated using the continuous variation method and stability constant at room temperature, and by using the mole ratio method, this value at 40°C was 1.24–2.4 × 108. In the formation of lead(II) metal complexes with 1-(aminomethyl) cyclohexene, Thanavelan et al. found the formation of their binary and ternary complexes. Glycine,
Using the stability constant method, these ternary complexes were found out, and using the parameters such as Δ log K and log X, these ternary complex data were compared with binary complex. The potentiometric technique at room temperature (25°C) was used in the investigation of some binary complex formations by Abdelatty Mohamed Radalla. These binary complexes are formed with 3D transition metal ions like Cu2+, Ni2+, Co2+, and Zn2+ and gallic acid’s importance as a ligand and 0.10 mol dm−3 of NaNO3. Such types of aliphatic dicarboxylic acids are very important biologically. Many acid-base characters and the nature of using metal complexes have been investigated and discussed time to time by researchers [7].
The above acids (gallic and aliphatic dicarboxylic acid) were taken to determine the acidity constants. For the purpose of determining the stability constant, binary and ternary complexes were carried in the aqueous medium using the experimental conditions as stated above. The potentiometric pH-metric titration curves are inferred for the binary complexes and ternary complexes at different ratios, and formation of ternary metal complex formation was in a stepwise manner that provided an easy way to calculate stability constants for the formation of metal complexes.
The values of Δ log K, percentage of relative stabilization (% R. S.), and log X were evaluated and discussed. Now it provides the outline about the various complex species for the formation of different solvents, and using the concentration distribution, these complexes were evaluated and discussed. The conductivity measurements have ascertained for the mode of ternary chelating complexes.
A study by Kathrina and Pekar suggests that pH plays an important role in the formation of metal complexes. When epigallocatechin gallate and gallic acid combine with copper(II) to form metal complexes, the pH changes its speculation. We have been able to determine its pH in frozen and fluid state with the help of multifrequency EPR spectroscopy [8]. With the help of this spectroscopy, it is able to detect that each polyphenol exhibits the formation of three different mononuclear species. If the pH ranges 4–8 for di- or polymeric complex of Cu(II), then it conjectures such metal complexes. It is only at alkaline pH values.
The line width in fluid solutions by molecular motion exhibits an incomplete average of the parameters of anisotropy spin Hamilton. If the complexes are different, then their rotational correlation times for this also vary. The analysis of the LyCEP anisotropy of the fluid solution spectra is performed using the parameters determined by the simulation of the rigid boundary spectra. Its result suggests that pH increases its value by affecting its molecular mass. It is a polyphenol ligand complex with copper, showing the coordination of an increasing number of its molecules or increasing participation of polyphenol dimers used as ligands in the copper coordination region.
The study by Vishenkova and his co-worker [8] provides the investigation of electrochemical properties of triphenylmethane dyes using a voltammetric method with constant-current potential sweep. Malachite green (MG) and basic fuchsin (BF) have been chosen as representatives of the triphenylmethane dyes [9]. The electrochemical behavior of MG and BF on the surface of a mercury film electrode depending on pH, the nature of background electrolyte, and scan rate of potential sweep has been investigated.
Using a voltammetric method with a constant-current potential sweep examines the electrical properties of triphenylmethane dye. In order to find out the solution of MG and BF, certain registration conditions have been prescribed for it, which have proved to be quite useful. The reduction peak for the currents of MG and BF has demonstrated that it increases linearly with respect to their concentration as 9.0 × 10−5–7.0 × 10−3 mol/dm3 for MG and 6.0 × 10−5–8.0 × 10−3 mol/dm3 for BF and correlation coefficients of these values are 0.9987 for MG and 0.9961 for BF [10].
5.0 × 10−5 and 2.0 × 10−5 mol/dm3 are the values used as the detection limit of MG and BF, respectively. Stability constants are a very useful technique whose size is huge. Due to its usefulness, it has acquired an umbrella right in the fields of chemistry, biology, and medicine. No science subject is untouched by this. Stability constants of metal complexes are widely used in the various areas like pharmaceuticals as well as biological processes, separation techniques, analytical processes, etc. In the presented chapter, we have tried to explain this in detail by focusing our attention on the applications and solutions of stability of metal complexes in solution.
Stability or formation or binding constant is the type of equilibrium constant used for the formation of metal complexes in the solution. Acutely, stability constant is applicable to measure the strength of interactions between the ligands and metal ions that are involved in complex formation in the solution [11]. A generally these 1-4 equations are expressed as the following ways:
Thus
K1, K2, K3, … Kn are the equilibrium constants and these are also called stepwise stability constants. The formation of the metal-ligand-n complex may also be expressed as equilibrium constants by the following steps:
The parameters K and β are related together, and these are expressed in the following example:
Now the numerator and denominator are multiplied together with the use of [metal-ligand] [metal-ligand2], and after the rearranging we get the following equation:
Now we expressed it as the following:
From the above relation, it is clear that the overall stability constant βn is equal to the product of the successive (i.e., stepwise) stability constants, K1, K2, K3,…Kn. This in other words means that the value of stability constants for a given complex is actually made up of a number of stepwise stability constants. The term stability is used without qualification to mean that the complex exists under a suitable condition and that it is possible to store the complex for an appreciable amount of time. The term stability is commonly used because coordination compounds are stable in one reagent but dissociate or dissolve in the presence of another regent. It is also possible that the term stability can be referred as an action of heat or light or compound. The stability of complex [13] is expressed qualitatively in terms of thermodynamic stability and kinetic stability.
In a chemical reaction, chemical equilibrium is a state in which the concentration of reactants and products does not change over time. Often this condition occurs when the speed of forward reaction becomes the same as the speed of reverse reaction. It is worth noting that the velocities of the forward and backward reaction are not zero at this stage but are equal.
If hydrogen and iodine are kept together in molecular proportions in a closed process vessel at high temperature (500°C), the following action begins:
In this activity, hydrogen iodide is formed by combining hydrogen and iodine, and the amount of hydrogen iodide increases with time. In contrast to this action, if the pure hydrogen iodide gas is heated to 500°C in the reaction, the compound is dissolved by reverse action, which causes hydrogen iodide to dissolve into hydrogen and iodine, and the ratio of these products increases over time. This is expressed in the following reaction:
For the formation of metal chelates, the thermodynamic technique provides a very significant information. Thermodynamics is a very useful technique in distinguishing between enthalpic effects and entropic effects. The bond strengths are totally effected by enthalpic effect, and this does not make any difference in the whole solution in order/disorder. Based on thermodynamics the chelate effect below can be best explained. The change of standard Gibbs free energy for equilibrium constant is response:
Where:
R = gas constant
T = absolute temperature
At 25°C,
ΔG = (− 5.708 kJ mol−1) · log β.
The enthalpy term creates free energy, i.e.,
For metal complexes, thermodynamic stability and kinetic stability are two interpretations of the stability constant in the solution. If reaction moves from reactants to products, it refers to a change in its energy as shown in the above equation. But for the reactivity, kinetic stability is responsible for this system, and this refers to ligand species [14].
Stable and unstable are thermodynamic terms, while labile and inert are kinetic terms. As a rule of thumb, those complexes which react completely within about 1 minute at 25°C are considered labile, and those complexes which take longer time than this to react are considered inert. [Ni(CN)4]2− is thermodynamically stable but kinetically inert because it rapidly exchanges ligands.
The metal complexes [Co(NH3)6]3+ and such types of other complexes are kinetically inert, but these are thermodynamically unstable. We may expect the complex to decompose in the presence of acid immediately because the complex is thermodynamically unstable. The rate is of the order of 1025 for the decomposition in acidic solution. Hence, it is thermodynamically unstable. However, nothing happens to the complex when it is kept in acidic solution for several days. While considering the stability of a complex, always the condition must be specified. Under what condition, the complex which is stable or unstable must be specified such as acidic and also basic condition, temperature, reactant, etc.
A complex may be stable with respect to a particular condition but with respect to another. In brief, a stable complex need not be inert and similarly, and an unstable complex need not be labile. It is the measure of extent of formation or transformation of complex under a given set of conditions at equilibrium [15].
Thermodynamic stability has an important role in determining the bond strength between metal ligands. Some complexes are stable, but as soon as they are introduced into aqueous solution, it is seen that these complexes have an effect on stability and fall apart. For an example, we take the [Co (SCN)4]2+ complex. The ion bond of this complex is very weak and breaks down quickly to form other compounds. But when [Fe(CN)6]3− is dissolved in water, it does not test Fe3+ by any sensitive reagent, which shows that this complex is more stable in aqueous solution. So it is indicated that thermodynamic stability deals with metal-ligand bond energy, stability constant, and other thermodynamic parameters.
This example also suggests that thermodynamic stability refers to the stability and instability of complexes. The measurement of the extent to which one type of species is converted to another species can be determined by thermodynamic stability until equilibrium is achieved. For example, tetracyanonickelate is a thermodynamically stable and kinetic labile complex. But the example of hexa-amine cobalt(III) cation is just the opposite:
Thermodynamics is used to express the difference between stability and inertia. For the stable complex, large positive free energies have been obtained from ΔG0 reaction. The ΔH0, standard enthalpy change for this reaction, is related to the equilibrium constant, βn, by the well thermodynamic equation:
For similar complexes of various ions of the same charge of a particular transition series and particular ligand, ΔS0 values would not differ substantially, and hence a change in ΔH0 value would be related to change in βn values. So the order of values of ΔH0 is also the order of the βn value.
Kinetic stability is referred to the rate of reaction between the metal ions and ligand proceeds at equilibrium or used for the formation of metal complexes. To take a decision for kinetic stability of any complexes, time is a factor which plays an important role for this. It deals between the rate of reaction and what is the mechanism of this metal complex reaction.
As we discuss above in thermodynamic stability, kinetic stability is referred for the complexes at which complex is inert or labile. The term “inert” was used by Tube for the thermally stable complex and for reactive complexes the term ‘labile’ used [16]. The naturally occurring chlorophyll is the example of polydentate ligand. This complex is extremely inert due to exchange of Mg2+ ion in the aqueous media.
The nature of central atom of metal complexes, dimension, its degree of oxidation, electronic structure of these complexes, and so many other properties of complexes are affected by the stability constant. Some of the following factors described are as follows.
In the coordination chemistry, metal complexes are formed by the interaction between metal ions and ligands. For these type of compounds, metal ions are the coordination center, and the ligand or complexing agents are oriented surrounding it. These metal ions mostly are the transition elements. For the determination of stability constant, some important characteristics of these metal complexes may be as given below.
Ligands are oriented around the central metal ions in the metal complexes. The sizes of these metal ions determine the number of ligand species that will be attached or ordinated (dative covalent) in the bond formation. If the sizes of these metal ions are increased, the stability of coordination compound defiantly decreased. Zn(II) metal ions are the central atoms in their complexes, and due to their lower size (0.74A°) as compared to Cd(II) size (0.97A°), metal ions are formed more stable.
Hence, Al3+ ion has the greatest nuclear charge, but its size is the smallest, and the ion N3− has the smallest nuclear charge, and its size is the largest [17]. Inert atoms like neon do not participate in the formation of the covalent or ionic compound, and these atoms are not included in isoelectronic series; hence, it is not easy to measure the radius of this type of atoms.
The properties of stability depend on the size of the metal ion used in the complexes and the total charge thereon. If the size of these metal ions is small and the total charge is high, then their complexes will be more stable. That is, their ratio will depend on the charge/radius. This can be demonstrated through the following reaction:
An ionic charge is the electric charge of an ion which is formed by the gain (negative charge) or loss (positive charge) of one or more electrons from an atom or group of atoms. If we talk about the stability of the coordination compounds, we find that the total charge of their central metal ions affects their stability, so when we change their charge, their stability in a range of constant can be determined by propagating of error [18]. If the charge of the central metal ion is high and the size is small, the stability of the compound is high:
In general, the most stable coordination bonds can cause smaller and highly charged rations to form more stable coordination compounds.
When an electron pair attracts a central ion toward itself, a strong stability complex is formed, and this is due to electron donation from ligand → metal ion. This donation process is increasing the bond stability of metal complexes exerted the polarizing effect on certain metal ions. Li+, Na+, Mg2+, Ca2+, Al3+, etc. are such type of metal cation which is not able to attract so strongly from a highly electronegative containing stable complexes, and these atoms are O, N, F, Au, Hg, Ag, Pd, Pt, and Pb. Such type of ligands that contains P, S, As, Br and I atom are formed stable complex because these accepts electron from M → π-bonding. Hg2+, Pb2+, Cd2+, and Bi3+ metal ions are also electronegative ions which form insoluble salts of metal sulfide which are insoluble in aqueous medium.
Volatile ligands may be lost at higher temperature. This is exemplified by the loss of water by hydrates and ammonia:
The transformation of certain coordination compounds from one to another is shown as follows:
A ligand is an ion or small molecule that binds to a metal atom (in chemistry) or to a biomolecule (in biochemistry) to form a complex, such as the iron-cyanide coordination complex Prussian blue or the iron-containing blood-protein hemoglobin. The ligands are arranged in spectrochemical series which are based on the order of their field strength. It is not possible to form the entire series by studying complexes with a single metal ion; the series has been developed by overlapping different sequences obtained from spectroscopic studies [19]. The order of common ligands according to their increasing ligand field strength is
The above spectrochemical series help us to for determination of strength of ligands. The left last ligand is as weaker ligand. These weaker ligand cannot forcible binding the 3d electron and resultant outer octahedral complexes formed. It is as-
Increasing the oxidation number the value of Δ increased.
Δ increases from top to bottom.
However, when we consider the metal ion, the following two useful trends are observed:
Δ increases with increasing oxidation number.
Δ increases down a group. For the determination of stability constant, the nature of the ligand plays an important role.
The following factors described the nature of ligands.
The size and charge are two factors that affect the production of metal complexes. The less charges and small sizes of ligands are more favorable for less stable bond formation with metal and ligand. But if this condition just opposite the product of metal and ligand will be a more stable compound. So, less nuclear charge and more size= less stable complex whereas if more nuclear charge and small in size= less stable complex. We take fluoride as an example because due to their smaller size than other halide and their highest electro negativity than the other halides formed more stable complexes. So, fluoride ion complexes are more stable than the other halides:
As compared to S2− ion, O22− ions formed more stable complexes.
It is suggested by Calvin and Wilson that the metal complexes will be more stable if the basic character or strength of ligands is higher. It means that the donating power of ligands to central metal ions is high [20].
It means that the donating power of ligands to central metal ions is high. In the case of complex formation of aliphatic diamines and aromatic diamines, the stable complex is formed by aliphatic diamines, while an unstable coordination complex is formed with aromatic diamines. So, from the above discussion, we find that the stability will be grater if the e-donation power is greater.
Thus it is clear that greater basic power of electron-donating species will form always a stable complex. NH3, CN−, and F− behaved as ligands and formed stable complexes; on the other hand, these are more basic in nature.
We know that if the concentration of coordination group is higher, these coordination compounds will exist in the water as solution. It is noted that greater coordinating tendency show the water molecules than the coordinating group which is originally present. SCN− (thiocynate) ions are present in higher concentration; with the Co2+ metal ion, it formed a blue-colored complex which is stable in state, but on dilution of water medium, a pink color is generated in place of blue, or blue color complex is destroyed by [Co(H2O)6]2+, and now if we added further SCN−, the pink color will not appear:
Now it is clear that H2O and SCN− are in competition for the formation of Co(II) metal-containing complex compound. In the case of tetra-amine cupric sulfate metal complex, ammonia acts as a donor atom or ligand. If the concentration of NH3 is lower in the reaction, copper hydroxide is formed but at higher concentration formed tetra-amine cupric sulfate as in the following reaction:
For a metal ion, chelating ligand is enhanced and affinity it and this is known as chelate effect and compared it with non-chelating and monodentate ligand or the multidentate ligand is acts as chelating agent. Ethylenediamine is a simple chelating agent (Figure 1).
Structure of ethylenediamine.
Due to the bidentate nature of ethylenediamine, it forms two bonds with metal ion or central atom. Water forms a complex with Ni(II) metal ion, but due to its monodentate nature, it is not a chelating ligand (Figures 2 and 3).
Structure of chelating configuration of ethylenediamine ligand.
Structure of chelate with three ethylenediamine ligands.
The dentate cheater of ligand provides bonding strength to the metal ion or central atom, and as the number of dentate increased, the tightness also increased. This phenomenon is known as chelating effect, whereas the formation of metal complexes with these chelating ligands is called chelation:
or
Some factors are of much importance for chelation as follows.
The sizes of the chelating ring are increased as well as the stability of metal complex decreased. According to Schwarzenbach, connecting bridges form the chelating rings. The elongated ring predominates when long bridges connect to the ligand to form a long ring. It is usually observed that an increased a chelate ring size leads to a decrease in complex stability.
He interpreted this statement. The entropy of complex will be change if the size of chelating ring is increased, i.e., second donor atom is allowed by the chelating ring. As the size of chelating ring increased, the stability should be increased with entropy effect. Four-membered ring compounds are unstable, whereas five-membered are more stable. So the chelating ring increased its size and the stability of the formed metal complexes.
The number of chelating rings also decides the stability of complexes. Non-chelating metal compounds are less stable than chelating compounds. These numbers increase the thermodynamic volume, and this is also known as an entropy term. In recent years ligands capable of occupying as many as six coordination positions on a single metal ion have been described. The studies on the formation constants of coordination compounds with these ligands have been reported. The numbers of ligand or chelating agents are affecting the stability of metal complexes so as these numbers go up and down, the stability will also vary with it.
For the Ni(II) complexes with ethylenediamine as chelating agent, its log K1 value is 7.9 and if chelating agents are trine and penten, then the log K1 values are 7.9 and 19.3, respectively. If the metal ion change Zn is used in place of Ni (II), then the values of log K1 for ethylenediamine, trine, and penten are 6.0, 12.1, and 16.2, respectively. The log βMY values of metal ions are given in Table 1.
Metal ion | log βMY (25°C, I = 0.1 M) |
---|---|
Ca2+ | 11.2 |
Cu2+ | 19.8 |
Fe3+ | 24.9 |
Metal ion vs. log βMY values.
Ni(NH3)62+ is an octahedral metal complex, and at 25 °C its log β6 value is 8.3, but Ni(ethylenediamine)32+ complex is also octahedral in geometry, with 18.4 as the value of log β6. The calculated stability value of Ni(ethylenediamine)32+ 1010 times is more stable because three rings are formed as chelating rings by ethylenediamine as compared to no such ring is formed. Ethylenediaminetetraacetate (EDTA) is a hexadentate ligand that usually formed stable metal complexes due to its chelating power.
A special effect in molecules is when the atoms occupy space. This is called steric effect. Energy is needed to bring these atoms closer to each other. These electrons run away from near atoms. There can be many ways of generating it. We know the repulsion between valence electrons as the steric effect which increases the energy of the current system [21]. Favorable or unfavorable any response is created.
For example, if the static effect is greater than that of a product in a metal complex formation process, then the static increase would favor this reaction. But if the case is opposite, the skepticism will be toward retardation.
This effect will mainly depend on the conformational states, and the minimum steric interaction theory can also be considered. The effect of secondary steric is seen on receptor binding produced by an alternative such as:
Reduced access to a critical group.
Stick barrier.
Electronic resonance substitution bond by repulsion.
Population of a conformer changes due to active shielding effect.
The macrocyclic effect is exactly like the image of the chelate effect. It means the principle of both is the same. But the macrocyclic effect suggests cyclic deformation of the ligand. Macrocyclic ligands are more tainted than chelating agents. Rather, their compounds are more stable due to their cyclically constrained constriction. It requires some entropy in the body to react with the metal ion. For example, for a tetradentate cyclic ligand, we can use heme-B which forms a metal complex using Fe+2 ions in biological systems (Figure 4).
Structure of hemoglobin is the biological complex compound which contains Fe(II) metal ion.
The n-dentate chelating agents play an important role for the formation of more stable metal complexes as compared to n-unidentate ligands. But the n-dentate macrocyclic ligand gives more stable environment in the metal complexes as compared to open-chain ligands. This change is very favorable for entropy (ΔS) and enthalpy (ΔH) change.
There are so many parameters to determination of formation constants or stability constant in solution for all types of chelating agents. These numerous parameters or techniques are refractive index, conductance, temperature, distribution coefficients, refractive index, nuclear magnetic resonance volume changes, and optical activity.
Solubility products are helpful and used for the insoluble salt that metal ions formed and complexes which are also formed by metal ions and are more soluble. The formation constant is observed in presence of donor atoms by measuring increased solubility.
To determine the solubility constant, it involves the distribution of the ligands or any complex species; metal ions are present in two immiscible solvents like water and carbon tetrachloride, benzene, etc.
In this method metal ions or ligands are present in solution and on exchanger. A solid polymers containing with positive and negative ions are ion exchange resins. These are insoluble in nature. This technique is helpful to determine the metal ions in resin phase, liquid phase, or even in radioactive metal. This method is also helpful to determine the polarizing effect of metal ions on the stability of ligands like Cu(II) and Zn(II) with amino acid complex formation.
At the equilibrium free metal and ions are present in the solution, and using the different electrometric techniques as described determines its stability constant.
This method is based upon the titration method or follows its principle. A stranded acid-base solution used as titrate and which is titrated, it may be strong base or strong acid follows as potentiometrically. The concentration of solution using 103− M does not decomposed during the reaction process, and this method is useful for protonated and nonprotonated ligands.
This is the graphic method used to determine the stability constant in producing metal complex formation by plotting a polarograph between the absences of substances and the presence of substances. During the complex formation, the presence of metal ions produced a shift in the half-wave potential in the solution.
If a complex is relatively slow to form and also decomposes at measurable rate, it is possible, in favorable situations, to determine the equilibrium constant.
This involves the study of the equilibrium constant of slow complex formation reactions. The use of tracer technique is extremely useful for determining the concentrations of dissociation products of the coordination compound.
This method is based on the study of the effect of an equilibrium concentration of some ions on the function at a definite organ of a living organism. The equilibrium concentration of the ion studied may be determined by the action of this organ in systems with complex formation.
The solution of 25 ml is adopted by preparing at the 1.0 × 10−5 M ligand or 1.0 × 10−5 M concentration and 1.0 × 10−5 M for the metal ion:
The solutions containing the metal ions were considered both at a pH sufficiently high to give almost complete complexation and at a pH value selected in order to obtain an equilibrium system of ligand and complexes.
In order to avoid modification of the spectral behavior of the ligand due to pH variations, it has been verified that the range of pH considered in all cases does not affect absorbance values. Use the collected pH values adopted for the determinations as well as selected wavelengths. The ionic strengths calculated from the composition of solutions allowed activity coefficient corrections. Absorbance values were determined at wavelengths in the range 430–700 nm, every 2 nm.
For a successive metal complex formation, use this method. If ligand is protonate and the produced complex has maximum number of donate atoms of ligands, a selective light is absorbed by this complex, while for determination of stability constant, it is just known about the composition of formed species.
Bjerrum (1941) used the method stepwise addition of the ligands to coordination sphere for the formation of complex. So, complex metal–ligand-n forms as the following steps [22]. The equilibrium constants, K1, K2, K3, … Kn are called stepwise stability constants. The formation of the complex metal-ligandn may also be expressed by the following steps and equilibrium constants.
Where:
M = central metal cation
L = monodentate ligand
N = maximum coordination number for the metal ion M for the ligand
If a complex ion is slow to reach equilibrium, it is often possible to apply the method of isotopic dilution to determine the equilibrium concentration of one or more of the species. Most often radioactive isotopes are used.
This method was extensively used by Werner and others to study metal complexes. In the case of a series of complexes of Co(III) and Pt(IV), Werner assigned the correct formulae on the basis of their molar conductance values measured in freshly prepared dilute solutions. In some cases, the conductance of the solution increased with time due to a chemical change, e.g.,
It is concluded that the information presented is very important to determine the stability constant of the ligand metal complexes. Some methods like spectrophotometric method, Bjerrum’s method, distribution method, ion exchange method, electrometric techniques, and potentiometric method have a huge contribution in quantitative analysis by easily finding the stability constants of metal complexes in aqueous solutions.
All the authors thank the Library of University of Delhi for reference books, journals, etc. which helped us a lot in reviewing the chapter.
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