Sustainable Processes for Critical Metal Recovery using Oxalate Chemistry

In the U.S., critical metals like aluminum (Al from bauxite), lithium (Li), cobalt (Co), and rare earth elements (REEs) are vital in various emerging technologies such as lithium-ion batteries (LIBs), solar cells, and high-tech electronics. Continuous growth in population with an increasing level of technological innovation has resulted in a rapid increase in resource consumption. Hence, sustainable, environmentally-friendly, and efficient use of available resources is required to preserve the natural resources for future generations. This dissertation introduces the utilization of oxalate chemistry to develop sustainable, environmentally-friendly, and closed-loop processes for recovery of critical metals like Li and Co from waste LIBs, Al and Fe from bauxite ore, and Fe and Ti from ilmenite ore. The oxalate anion (C2O42-) can be derived from organic sources, has minimal environmental impact, and forms moderately acidic reagents like oxalic acid (H2C2O4), potassium hydrogen oxalate (KHC2O4), and ammonium hydrogen oxalate (NH4HC2O4). The oxalate reagents are known for the chelation and precipitation properties, but the leaching and reduction properties had not been previously studied. This dissertation establishes oxalate reagents as an efficient route to recover and separate metals from various mixed metal oxide sources.The demand for LIBs has significantly increased over the last 5 years, leading to a shortage in the supply of Li and Co. The LIBs economy can be stabilized by recycling the critical metals from spent cathodes. Currently, approximately 59% of the LIBs contain lithium cobalt oxide (LiCoO2) as the cathode material. In this work, oxalate chemistry has been used to recover and separate Li and Co from LiCoO2. Traditionally, inorganic acids like sulfuric and nitric acid with a reducing agent like hydrogen peroxide (H2O2) are used to recycle LIBs, but the emission of harmful pollutants like SOX and NOX pose a significant risk to the environment. The clean and green oxalate reagents like H2C2O4 and NH4HC2O4 can extract Li into the aqueous phase as lithium oxalate (Li2C2O4) and precipitate cobalt oxalate (CoC2O4·2H2O) from LiCoO2 in a single step. The optimum acidity for Li and Co extraction and separation using oxalate reagents was pH 13. The Co was separated from CoC2O4·2H2O by dissolving and precipitating the metal oxalate (as Co(OH)2) in the basic solution recovered after Li precipitation. Alternatively, micro-rod structure Co3O4 was synthesized by calcining CoC2O4·2H2O at T > 400 °C in the presence of air. In this work, oxalate chemistry was also used for efficient Fe and Al recovery from bauxite ore. Bauxite ore is the world’s primary source for Al metal, and the Bayer process (based on NaOH) holds an exclusive status for its refining. The Bayer process is efficient for Al extraction, but a massive quantity of “red mud” waste is generated. The red mud is an iron-containing caustic waste and is typically disposed in landfills or open ponds and reservoirs. The high alkalinity of the waste pollutes the land and ecosystem around it. With the growing demand for Al, the disposal methods of red mud needs global attention. Using oxalate chemistry, reagents like H2C2O4, KHC2O4, and H2C2O4∙KHC2O4 can efficiently recover Fe and Al from bauxite ore. From NIST SRM 600 bauxite ore, more than 90% of Fe and Al was extracted into the aqueous phase in less than 2 h with 0.50 M C2O42- at 100 °C for all three reagents. Among the three oxalate reagents, H2C2O4 is the most acidic, followed by H2C2O4∙KHC2O4 and KHC2O4. The Fe can be selectively precipitated by hydrolyzing the aqueous phase to a pH = 13.80. After separating the Fe precipitate, the resulting filtrate can be acidified to a pH = 10.50 for efficient Al precipitation. The recycling of acid after the efficient metal extractions is critical to minimize waste generation and improve economics. In this work, two unique acid recycling processes were developed to efficiently recover and reuse oxalate reagents. The first process utilizes strong acid cation-exchange resins to regenerate the oxalate reagent in acidic form. The amount of resins determines the final pH and the type of oxalate reagent regenerated. To recycle the aqueous phase as H2C2O4, a pH 400 °C in the presence of air. In this work, oxalate chemistry was also used for efficient Fe and Al recovery from bauxite ore. Bauxite ore is the world’s primary source for Al metal, and the Bayer process (based on NaOH) holds an exclusive status for its refining. The Bayer process is efficient for Al extraction, but a massive quantity of “red mud” waste is generated. The red mud is an iron-containing caustic waste and is typically disposed in landfills or open ponds and reservoirs. The high alkalinity of the waste pollutes the land and ecosystem around it. With the growing demand for Al, the disposal methods of red mud needs global attention. Using oxalate chemistry, reagents like H2C2O4, KHC2O4, and H2C2O4∙KHC2O4 can efficiently recover Fe and Al from bauxite ore. From NIST SRM 600 bauxite ore, more than 90% of Fe and Al was extracted into the aqueous phase in less than 2 h with 0.50 M C2O42- at 100 °C for all three reagents. Among the three oxalate reagents, H2C2O4 is the most acidic, followed by H2C2O4∙KHC2O4 and KHC2O4. The Fe can be selectively precipitated by hydrolyzing the aqueous phase to a pH = 13.80. After separating the Fe precipitate, the resulting filtrate can be acidified to a pH = 10.50 for efficient Al precipitation. The recycling of acid after the efficient metal extractions is critical to minimize waste generation and improve economics. In this work, two unique acid recycling processes were developed to efficiently recover and reuse oxalate reagents. The first process utilizes strong acid cation-exchange resins to regenerate the oxalate reagent in acidic form. The amount of resins determines the final pH and the type of oxalate reagent regenerated. To recycle the aqueous phase as H2C2O4, a pH < 1.0 was optimal, whereas, for KHC2O4 and NH4HC2O4, a pH around 2.5 was required. Additionally, the low aqueous solubility of KHC2O4 and H2C2O4∙KHC2O4 was utilized to precipitate 60-80% of acid by acidifying the aqueous phase after metal recovery to a pH = 1.5-2.5. Due to acid recycling, the H2C2O4 + H2O2 process for LIBs recycling produced 50% less waste than the traditional H2SO4 process at a similar cost. The oxalate processes demonstrated in this work were closed-loop, environmentally-friendly, and economical and can offer similar advantages for recycling valuable metals from various waste streams

INSPIRESat-1 Nanosat Mission

The INSPIRE, International Satellite Program in Research and Education, is a group of six institutes namely Laboratory for Atmospheric and Space Physics (LASP), USA, Indian Institute of Space Science and Technology (IIST), India, National Central University (NCU), Taiwan. Nanyang Technological University (NTU), Singapore, Sultan Qaboos University, Oman, Le Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), France. Three institutions LASP, IIST and NCU are developing the first satellite of the program called the INSPIRESat-1, whose mission details and design are presented in this paper. The paper gives the details of payload, its mode of operations and its interface with other subsystems, Command and Data Handling (C&DH) system and its capabilities, Electrical Power Supply (EPS) subsystem, access and power analysis, and modes of operation. It also describes Flight Software (FSW), communication, Attitude Determination and Control System (ADCS), and structures

Cloud Computing Systems Exploration over Workload Prediction Factor in Distributed Applications

This paper highlights the different techniques of workload prediction in cloud computing. Cloud computing resources have a special kind of arrangement in which resources are made available on demand to the customers. Today, most of the organizations are using cloud computing that results in reduction of the operational cost. Cloud computing also reduces the overhead of any organization due to implementation of many hardware and software platforms. These services are being provided by cloud provider on the basis of pay per use. There are lots of cloud service providers in the modern era. In this competitive era, every cloud provider works to provide better services to the customer. To fulfill the customer?s requirements, dynamic provisioning can serve the purpose in cloud system where resources can be released and allocated on later stage as per needs. That?s why resource scaling becomes a great challenge for the cloud providers. There are many approaches to scale the number of instances of any resource. Two main approaches namely: proactive and reactive are used in cloud systems. Reactive approach reacts at later stage while proactive approach predicts resources in advance. Cloud provider needs to predict the number of resources in advance that an application is intended to use. Historical data and patterns can be used for the workload prediction. The benefit of the proactive approach lies in advance number of instances of a resource available for the future use. This results in improved performance for the cloud systems

Prediction of the structural and electronic properties of MoxTi1−xS2 monolayers via first principle simulations

Two-dimensional transition metal dichalcogenides have gained great attention because of their peculiar physical properties that make them interesting for a wide range of applications. Lately, alloying between different transition metal dichalcogenides has been proposed as an approach to control two-dimensional phase stability and to obtain compounds with tailored characteristics. In this theoretical study, we predict the phase diagram and the electronic properties of MoxTi1−xS2 at varying stoichiometry and show how the material is metallic, when titanium is the predominant species, while it behaves as a p-doped semiconductor, when approaching pure MoS2 composition. Correspondingly, the thermodynamically most stable phase switches from the tetragonal to the hexagonal one. Further, we present an example which shows how the proposed alloys can be used to obtain new vertical two-dimensional heterostructures achieving effective electron/hole separation

ALTERNATE POWER AND ENERGY STORAGE/REUSE FOR DRILLING RIGS: REDUCED COST AND LOWER EMISSIONS PROVIDE LOWER FOOTPRINT FOR DRILLING OPERATIONS

Diesel engines operating the rig pose the problems of low efficiency and large amount of emissions. In addition the rig power requirements vary a lot with time and ongoing operation. Therefore it is in the best interest of operators to research on alternate drilling energy sources which can make entire drilling process economic and environmentally friendly. One of the major ways to reduce the footprint of drilling operations is to provide more efficient power sources for drilling operations. There are various sources of alternate energy storage/reuse. A quantitative comparison of physical size and economics shows that rigs powered by the electrical grid can provide lower cost operations, emit fewer emissions, are quieter, and have a smaller surface footprint than conventional diesel powered drilling. This thesis describes a study to evaluate the feasibility of adopting technology to reduce the size of the power generating equipment on drilling rigs and to provide ?peak shaving? energy through the new energy generating and energy storage devices such as flywheels. An energy audit was conducted on a new generation light weight Huisman LOC 250 rig drilling in South Texas to gather comprehensive time stamped drilling data. A study of emissions while drilling operation was also conducted during the audit. The data was analyzed using MATLAB and compared to a theoretical energy audit. The study showed that it is possible to remove peaks of rig power requirement by a flywheel kinetic energy recovery and storage (KERS) system and that linking to the electrical grid would supply sufficient power to operate the rig normally. Both the link to the grid and the KERS system would fit within a standard ISO container. A cost benefit analysis of the containerized system to transfer grid power to a rig, coupled with the KERS indicated that such a design had the potential to save more than $10,000 per week of drilling operations with significantly lower emissions, quieter operation, and smaller size well pad

Auditing Gender Analyzers on Text Data

AI models have become extremely popular and accessible to the general public. However, they are continuously under the scanner due to their demonstrable biases toward various sections of the society like people of color and non-binary people. In this study, we audit three existing gender analyzers -- uClassify, Readable and HackerFactor, for biases against non-binary individuals. These tools are designed to predict only the cisgender binary labels, which leads to discrimination against non-binary members of the society. We curate two datasets -- Reddit comments (660k) and, Tumblr posts (2.05M) and our experimental evaluation shows that the tools are highly inaccurate with the overall accuracy being ~50% on all platforms. Predictions for non-binary comments on all platforms are mostly female, thus propagating the societal bias that non-binary individuals are effeminate. To address this, we fine-tune a BERT multi-label classifier on the two datasets in multiple combinations, observe an overall performance of ~77% on the most realistically deployable setting and a surprisingly higher performance of 90% for the non-binary class. We also audit ChatGPT using zero-shot prompts on a small dataset (due to high pricing) and observe an average accuracy of 58% for Reddit and Tumblr combined (with overall better results for Reddit). Thus, we show that existing systems, including highly advanced ones like ChatGPT are biased, and need better audits and moderation and, that such societal biases can be addressed and alleviated through simple off-the-shelf models like BERT trained on more gender inclusive datasets.Comment: This work has been accepted at IEEE/ACM ASONAM 2023. Please cite the version appearing in the ASONAM proceeding

A study on prevalence of metabolic syndrome in general population in Western Uttar Pradesh, India

Background: A global transition in the disease pattern has been observed, where the relative impact of infectious disease is decreasing while chronic disease like cardiovascular disease and diabetes are increasingly dominating the disease pattern. Metabolic syndrome (MetS) is a complex web of metabolic factors that are associated with a 2-fold increased risk of CVD and 5-fold risk of diabetes.Methods: A total of 350 patients were included and a cross sectional study was conducted to identify metabolic syndrome prevalence and to evaluate risk factor for development of metabolic syndrome.Results: 48% of participants were male and 52% were female our results showed a BMI of 22.11±3.57 in male and 22.16±2.82 in females. The overall prevalence of overweight was 31%. This study also showed significantly higher rate of metabolic syndrome in older age group i.e. 9.38% in age group of 30-39 years and 26.98% in 60-70 age group. Proportionally more subjects with MetS (74%) have sedentary life style as compared to those without MetS (54%).Conclusions: The prevalence of Metabolic syndrome varies among ethnic groups. Indians are at high risk for ASCVD and their predisposition. The high prevalence of overweight and obesity was one of the major driving force in the development of MetS

Determination of genetic divergence in pointed gourd by principal component and non-hierarchical euclidean cluster analysis

The present investigation was carried out at Vegetable Research Centre, Pantnagar to estimate the ge-netic divergence using Mahalanobis D2 statistics for twelve characters on 35 genotypes of pointed gourd. Cluster analysis and principal component analysis (PCA) were used to identify the most discerning trait responsible for greater variability in the lines and on the basis of mean performance, genotypes were classified into different groups. Five principal components (PC) have been extracted using the mean performance of the genotypes and 83.23 per cent variation is yielded by the first five principal components, among them high mean positive value or higher weight age was obtained was obtained for days to first female flower anthesis and days to first fruit harvest among all the vectors, indicates that these traits are important component of genetic divergence in pointed gourd. Non- hierarchical Euclidean cluster analysis grouped the genotypes into seven clusters and the highest number of genotypes were found in cluster number IV i.e. eleven whereas maximum inter-cluster distance was found between the cluster III and VI i.e. 74.250, it indicates that a wide range of genetic divergence is present between the genotypes present among these two clusters. And as per contribution toward total divergence, traits like fruit yield per hectare and number of fruit per plant contributed 92.64% toward total divergence. The high diversity found in the genotypes showed its great potential for improving qualitative as well as quantitative traits in pointed gourd