results 37-48 of 557
Bioinspired Metallopeptide Hydrogels for Value Added Chemicals
23/11/2023
Inventors: Dr. Subhasish Roy
Bioinspired metallopeptide hydrogels represent a promising avenue for producing value-added chemicals. These hydrogels are designed using peptide sequences that incorporate metal ions, mimicking natural metalloenzymes' catalytic properties. By immobilizing metal ions within the peptide matrix, these materials exhibit enhanced catalytic activity and selectivity for various chemical transformations, such as oxidation,...
Bioinspired metallopeptide hydrogels represent a promising avenue for producing value-added chemicals. These hydrogels are designed using peptide sequences that incorporate metal ions, mimicking natural metalloenzymes' catalytic properties. By immobilizing metal ions within the peptide matrix, these materials exhibit enhanced catalytic activity and selectivity for various chemical transformations, such as oxidation, reduction, and carbon-carbon bond formation. The structural flexibility of peptides allows precise control over the catalytic environment, optimizing reaction conditions for specific chemical targets. This approach not only enhances reaction efficiency but also offers a sustainable route to producing high-value chemicals, aligning with green chemistry principles and advancing the field of biomimetic catalysis. (Chemistry)
Exploring the reactivity of Amino acid and peptide-based multifunctional molecular electrocatalysts for Energy conversion
13/11/2023
Inventors: Dr. Sudipta Chaterjee
Exploring the reactivity of amino acid and peptide-based multifunctional molecular electrocatalysts for energy conversion reveals promising advancements in sustainable energy technologies. These catalysts leverage the inherent versatility of amino acids and peptides to facilitate efficient electron transfer processes in electrochemical reactions such as water splitting, CO2 reduction, and oxygen evolution....
Exploring the reactivity of amino acid and peptide-based multifunctional molecular electrocatalysts for energy conversion reveals promising advancements in sustainable energy technologies. These catalysts leverage the inherent versatility of amino acids and peptides to facilitate efficient electron transfer processes in electrochemical reactions such as water splitting, CO2 reduction, and oxygen evolution. Their molecular design allows for precise tuning of active sites and electronic properties, optimizing catalytic performance for enhanced efficiency and selectivity. By harnessing renewable energy sources like solar and wind power, these electrocatalysts contribute to the development of clean energy solutions, addressing global energy demands while minimizing environmental impact. (Chemistry)
Deep Eutectic Solvents for Flow Electrode Capacitive Deionization (FCDI): An Innovative Concept for Electrode Driven High-Capacity Desalination
15/12/2023
Inventors: Dr. Upasana Mahanta
Deep eutectic solvents (DES) offer a novel approach in Flow Electrode Capacitive Deionization (FCDI), revolutionizing electrode-driven desalination methods. By replacing conventional electrolytes with DES, FCDI systems achieve higher capacity and efficiency in removing salts from water. DES, composed of low-cost and environmentally friendly components, enhance ion adsorption and desorption kinetics...
Deep eutectic solvents (DES) offer a novel approach in Flow Electrode Capacitive Deionization (FCDI), revolutionizing electrode-driven desalination methods. By replacing conventional electrolytes with DES, FCDI systems achieve higher capacity and efficiency in removing salts from water. DES, composed of low-cost and environmentally friendly components, enhance ion adsorption and desorption kinetics at the electrodes. This innovation optimizes energy consumption and operational costs while extending electrode lifespan. The integration of DES in FCDI paves the way for sustainable desalination technologies, promising significant advancements in water purification for regions facing freshwater scarcity. (Chemical Engineering)
Development of E-HFQ Process for Precision Manufacturing of Automotive Body Components using High-Strength Aluminum Alloy Sheets
15/12/2023
Inventors: Dr. Sudhy S Panicker
The development of the E-HFQ (Electromagnetic Hot Forming Quenching) process for precision manufacturing of automotive body components using high-strength aluminum alloy sheets combines electromagnetic heating, hot forming, and rapid quenching. This innovative approach enhances formability and strength properties of aluminum alloys, critical for lightweight vehicle construction. E-HFQ optimizes heating uniformity...
The development of the E-HFQ (Electromagnetic Hot Forming Quenching) process for precision manufacturing of automotive body components using high-strength aluminum alloy sheets combines electromagnetic heating, hot forming, and rapid quenching. This innovative approach enhances formability and strength properties of aluminum alloys, critical for lightweight vehicle construction. E-HFQ optimizes heating uniformity and cycle times, reducing energy consumption and production costs compared to conventional methods. The process's precision allows for complex part geometries and tight tolerances, improving structural integrity and crashworthiness of automotive bodies. Its advancement represents a significant step towards sustainable manufacturing practices in the automotive industry, promoting efficiency and performance in high-strength aluminum alloy applications. (Mechanical Engineering)
Low-cost Environmental Monitoring System powered by Solar Energy
17/11/2023
Inventors: Dr. Anita Agarwal, Dr. Rajiv Kumar Chaturvedi
A low-cost environmental monitoring system powered by solar energy integrates sensors for measuring air quality, temperature, humidity, and other parameters. The system includes a solar panel for energy supply, a battery for energy storage, and wireless connectivity for data transmission. Its design emphasizes affordability, utilizing off-the-shelf components and open-source software...
A low-cost environmental monitoring system powered by solar energy integrates sensors for measuring air quality, temperature, humidity, and other parameters. The system includes a solar panel for energy supply, a battery for energy storage, and wireless connectivity for data transmission. Its design emphasizes affordability, utilizing off-the-shelf components and open-source software for data processing and visualization. The system is scalable for deployment in various environments, providing real-time data for environmental analysis and decision-making. Solar power ensures autonomy and sustainability, making it suitable for remote locations without reliable grid access. Overall, it offers a cost-effective solution for continuous environmental monitoring and management. (EEE)
Design, Development, And Performance Investigation Of Neck Exoskeleton In Prevention And Reduction Of The Neck Pain
09/11/2023
Inventors: Dr. Ganesh Bapat, Dr. Jajati Sahoo
The design, development, and performance investigation of a neck exoskeleton for preventing and reducing neck pain involves several key aspects. Initially, biomechanical analysis identifies stress points and movements causing neck pain. The exoskeleton design integrates lightweight materials for comfort and adjustable mechanisms for personalized fit and support. Development includes prototyping...
The design, development, and performance investigation of a neck exoskeleton for preventing and reducing neck pain involves several key aspects. Initially, biomechanical analysis identifies stress points and movements causing neck pain. The exoskeleton design integrates lightweight materials for comfort and adjustable mechanisms for personalized fit and support. Development includes prototyping and iterative testing to optimize ergonomics and functionality. Performance investigation assesses the exoskeleton's effectiveness through clinical trials and ergonomic evaluations, measuring factors like pain reduction, range of motion improvement, and user satisfaction. Continuous refinement based on feedback ensures the exoskeleton meets ergonomic needs, enhancing its utility in preventing and managing neck pain effectively. (Mechanical Engineering)
Polyoxo-Noble-Metalate (PONM) and polyoxometalate (POM) – Based dual-vertex Metal organic frameworks (PONM-POM -MOFs) and POM–MOFs with Ligand Anchored noble -Metal Single-sites: Novel Classes of Heterogenous Catalysts
11/10/2023
Inventors: Dr. Saurav Bhattacharya
Polyoxo-noble-metalates (PONMs) and polyoxometalates (POMs) serve as foundational components in the design of dual-vertex metal-organic frameworks (MOFs) and POM-MOFs with ligand-anchored noble-metal single-sites. These novel catalysts integrate the unique catalytic properties of POMs/PONMs with the structural flexibility and porous nature of MOFs. The dual-vertex architecture enhances catalytic activity by providing...
Polyoxo-noble-metalates (PONMs) and polyoxometalates (POMs) serve as foundational components in the design of dual-vertex metal-organic frameworks (MOFs) and POM-MOFs with ligand-anchored noble-metal single-sites. These novel catalysts integrate the unique catalytic properties of POMs/PONMs with the structural flexibility and porous nature of MOFs. The dual-vertex architecture enhances catalytic activity by providing multiple active sites and enabling synergistic interactions between the POM/PONM clusters and anchored noble-metal sites. These heterogeneous catalysts offer enhanced selectivity and efficiency in various chemical transformations, promising advancements in green chemistry and industrial processes requiring precise control over catalytic activity and stability. (Chemistry)
Optimizations for Compression using Zstandard
21/09/2023
Inventors: Dr. Vinayak Naik, Dr. Arnab K Paul
Optimizing compression with Zstandard involves leveraging its adaptive compression level selection and efficient dictionary management. By adjusting parameters like compression level and window size dynamically, Zstandard balances between compression ratio and speed, catering to diverse data types effectively. Its ability to maintain high throughput and low latency makes it ideal...
Optimizing compression with Zstandard involves leveraging its adaptive compression level selection and efficient dictionary management. By adjusting parameters like compression level and window size dynamically, Zstandard balances between compression ratio and speed, catering to diverse data types effectively. Its ability to maintain high throughput and low latency makes it ideal for real-time applications and large-scale data processing tasks. Furthermore, Zstandard's seamless integration with existing frameworks and support for multiple platforms ensure versatility and performance consistency across various computing environments, enhancing overall data storage and transmission efficiencies. (CS&IS)
Sensor Based Data Augmentation To Improve Machine Vision Algorithms For Large Scale Agronomic Semantic Segmentation Of Agricultural Farmlands
26/09/2023
Inventors: Dr. Sravan Danda, Dr. Sougat Sen
Sensor-based data augmentation enhances machine vision algorithms for agronomic semantic segmentation in large-scale agricultural settings. By integrating multispectral, hyperspectral, LiDAR, and thermal sensor data, algorithms gain comprehensive insights into crop health, terrain, and environmental factors. This fusion enriches the training dataset, improving model accuracy in identifying crop types, growth stages,...
Sensor-based data augmentation enhances machine vision algorithms for agronomic semantic segmentation in large-scale agricultural settings. By integrating multispectral, hyperspectral, LiDAR, and thermal sensor data, algorithms gain comprehensive insights into crop health, terrain, and environmental factors. This fusion enriches the training dataset, improving model accuracy in identifying crop types, growth stages, and anomalies like stress or disease. This approach not only enhances precision agriculture practices but also supports optimal resource allocation and yield prediction, crucial for sustainable and efficient farming operations on expansive farmlands. (CS&IS)
Developing Efficient Methods for Approximating Generalized Inverses of Tensors and Solving Multilinear Systems
26/09/2023
Inventors: Dr. Jajati Keshari Sahoo
Developing efficient methods for approximating generalized inverses of tensors and solving multilinear systems is crucial for various applications in data analysis, signal processing, and machine learning. One approach involves leveraging tensor decomposition techniques such as CP (CANDECOMP/PARAFAC) decomposition or Tucker decomposition to approximate tensor inverses. These methods reduce the computational...
Developing efficient methods for approximating generalized inverses of tensors and solving multilinear systems is crucial for various applications in data analysis, signal processing, and machine learning. One approach involves leveraging tensor decomposition techniques such as CP (CANDECOMP/PARAFAC) decomposition or Tucker decomposition to approximate tensor inverses. These methods reduce the computational complexity by decomposing high-dimensional tensors into lower-dimensional components, facilitating easier manipulation and inversion.
For solving multilinear systems, iterative methods like alternating least squares (ALS) or optimization-based approaches can be employed. These algorithms iteratively update tensor factors or variables to minimize a cost function, thereby solving the system efficiently. Additionally, regularization techniques and parallel computing can enhance computational efficiency and scalability in large-scale tensor operations.
Overall, combining tensor decomposition with iterative optimization methods and parallel computing offers a promising avenue for developing efficient algorithms to approximate tensor inverses and solve multilinear systems effectively. These advancements are essential for tackling complex problems in multidimensional data analysis and modeling. (Mathematics)
HEXR: Hybrid Explainable Robust Learning Framework to Reduce Annotation Overhead
26/09/2023
Inventors: Dr. Surjya Ghosh, Dr. Snehanshu Saha
HEXR, the Hybrid Explainable Robust Learning Framework, addresses the challenge of reducing annotation overhead in machine learning. It combines robust learning algorithms with explainable AI techniques to optimize model performance with minimal labeled data. By leveraging robust methods like adversarial training and domain adaptation, HEXR enhances model resilience against data...
HEXR, the Hybrid Explainable Robust Learning Framework, addresses the challenge of reducing annotation overhead in machine learning. It combines robust learning algorithms with explainable AI techniques to optimize model performance with minimal labeled data. By leveraging robust methods like adversarial training and domain adaptation, HEXR enhances model resilience against data perturbations and distribution shifts. Simultaneously, its explainable AI components provide insights into model decisions, improving transparency and trustworthiness. This hybrid approach not only lowers the annotation burden but also enhances the reliability and interpretability of machine learning models, making them suitable for practical applications where data annotation is costly or limited. (CS&IS)
Manipulation Of Particles In Mircochannels For Point Of Care Diagnostic Devices
26/09/2023
Inventors: Dr. Nilesh D. Pawar
Manipulating particles in microchannels for point-of-care diagnostic devices involves precise control techniques to enhance efficiency and accuracy. Utilizing principles like electrokinetics, magnetophoresis, and acoustic manipulation allows for sorting, concentrating, and detecting target particles within microfluidic systems. These techniques optimize sample handling, reducing processing times and improving sensitivity in detecting biomarkers...
Manipulating particles in microchannels for point-of-care diagnostic devices involves precise control techniques to enhance efficiency and accuracy. Utilizing principles like electrokinetics, magnetophoresis, and acoustic manipulation allows for sorting, concentrating, and detecting target particles within microfluidic systems. These techniques optimize sample handling, reducing processing times and improving sensitivity in detecting biomarkers or pathogens. Integration with microfabrication technologies enables miniaturized devices capable of rapid, portable diagnostics for various diseases. This manipulation capability in microchannels underpins advancements in point-of-care diagnostics, facilitating timely and reliable medical testing in diverse settings beyond traditional laboratory environments. (Mechanical Engineering)
An Institute of Eminence
