Browsing by Author "Nishant Singhal"

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AI-Enabled Nano Biosensors for Estimating Heavy Metal Contamination in Crops
(Springer, 2025) Nishant Singhal; Harsh Vardhan; Rajul Jain; Payal Gupta; Ashish Gaur; Suresh Kushinath Ghotekar; Deepak Kumar
Heavy metal pollution poses a serious global threat to food safety and sustainable agriculture. Routine evaluation of harmful substances such as cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) in crops is essential to prevent their accumulation in the food supply. Conventional detection techniques, including atomic absorption spectrometry and ICP-MS, are often expensive, labor-intensive, and unsuitable for field applications. In this context, merging artificial intelligence (AI) with nano biosensors introduces an innovative approach for rapid and precise detection. Nano biosensors, which utilize nanomaterials alongside biorecognition elements, provide remarkable sensitivity and specificity for identifying various heavy metals, even at minimal concentrations. When combined with AI and machine learning, these sensors allow for instant data processing, predictive analysis, and spatial mapping of contaminated sites. These real-time observations empower farmers and environmental organizations to make time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Algorithms for nature: integrating technology, ecology, and society for sustainable conservation
(Springer Medizin, 2025) Nishant Singhal; Harsh Vardhan; Rajul Jain; Piyush Vashistha; Aaysha Pandey; Naresh Kumar Wagri; Ashish Gaur
To safeguard ecosystems amid rapid global changes, strategies must link ecological knowledge with advancements in technology. Traditional ecological models often encounter challenges due to the inherent complexity and unpredictability of ecosystems, limiting their ability to guide large-scale, long-term decisions effectively. Emerging technologies such as optimization algorithms, artificial intelligence, and big data analytics provide ways to address these issues by improving forecasting, monitoring, and management in evolving environments. The application of these technologies has broadened to essential areas like ecological restoration, management of invasive species, carbon capture, fisheries management, and wildfire readiness, enhancing effectiveness, accuracy, and scalability in conservation efforts. Beyond technical improvements, the integration of algorithms with ecosystem science highlights the importance of aligning data-driven strategies with socio-ecological realities, where careful consideration of trade-offs between biodiversity, economic gains, and resilience is essential. This review points out that algorithmic methods do not replace ecological expertise but rather expand its scope, enabling innovative avenues for adaptive, inclusive, and sustainable conservation practices. By embedding computational innovations within ecological and social contexts, it reveals pathways to more effective strategies that can address the urgent challenges of biodiversity conservation in the 21st century. © The Author(s) 2025.
Cultivation to consumption: strengthening bacterial safety in plant-based nutraceuticals
(Frontiers Media SA, 2025) Ashish Gaur; Nishant Singhal; Harsh Vardhan; Rajul Jain; Yograj Bist; Naresh Kumar Wagri
Plant-based nutraceuticals are increasingly recognized for their bioactive compounds that promote health and assist in preventing chronic diseases. However, the rising demand has raised concerns about microbial safety, as contamination can occur at multiple stages of the production process-ranging from cultivation and harvesting to processing, storage, and distribution. Pathogens such as Escherichia coli, Salmonella, Listeria monocytogenes, and toxin-producing fungi pose risks to product quality, threaten consumer health, and contribute to antimicrobial resistance. This review provides a comprehensive overview of the sources and types of microbial contamination, associated health risks, and the shortcomings of conventional control methods. It highlights recent advancements in safety techniques, including cold plasma, ultraviolet light treatment, high hydrostatic pressure, nanocoatings, probiotic biocontrol, and AI-driven microbial monitoring. Additionally, the analysis addresses the role of regulatory frameworks, quality assurance practices, and consumer education as integral elements of a unified safety approach. It integrates technological progress, regulatory perspectives, and consumer behavior to offer a detailed guide for ensuring the microbial safety of plant-derived nutraceuticals, thereby fostering confidence in these products from production through to consumption. © © 2025 Gaur, Singhal, Vardhan, Jain, Bist and Wagri.
Microplastics to Metabolomics: Understanding the environmental and health implications of plastic pollution
(Elsevier Ltd, 2025) Ashish Gaur; Nishant Singhal; Rajat Singh; Rajul Jain; Narpinder Singh; Gaurav Pant; A. Karnwal; T. Malik
Microplastics (MPs) are composed of solid plastic fragments that are smaller than 5 mm and are insoluble in water. MPs have acquired more attention in recent years, due to their pervasiveness in the environment. There are worries regarding the potential health effects of these particles on humans because they have been discovered in various food products and water sources. So, a comprehensive method for researching the biochemical effects of MP exposure is provided by metabolomics, an analytical technique that is useful for analyzing metabolites within biological systems. This review explores the application of metabolomics to the investigation of the biological consequences of Microplastic (MP) pollution. It looks at metabolomics methods, both targeted and untargeted, that offer a thorough examination of the metabolites impacted by MP exposure. The review also examines the potential health risks—such as oxidative stress, immune system disruption, and metabolic problems—associated with MP exposure. It highlights the need for additional study to elucidate the long-term health consequences of MP exposure. This review provides a comprehensive understanding of the biological effects of MP contamination and its wider ecological and health implications by combining metabolomics with environmental research. © 2025 The Authors
Polysaccharide-based functional materials for flexible electronics: A comprehensive review of synthesis strategies, functionalization, and applications
(Elsevier Ltd, 2025) Rajul Jain; Nishant Singhal; Harsh Vardhan; Piyush Vashistha; Yograj Bist; Aaysha Pandey; Naresh Kumar Wagri; Ashish Gaur
Polysaccharides, as abundant and renewable biopolymers, have increasingly attracted attention for their potential uses in flexible electronics due to their sustainability, adaptability, and versatile functionality. Natural polymers, including cellulose, chitosan, alginate, starch, and hemicellulose, exhibit key characteristics such as biodegradability, biocompatibility, and tunable mechanical properties, making them attractive choices for advanced technological applications. Advances in chemical modification, blending, and nano structuring have led to improvements in conductivity, durability, and flexibility, broadening their use in areas such as wearable sensors, medical devices, energy storage solutions, and smart packaging. Recent research highlights strategies to overcome inherent challenges like low conductivity and sensitivity to environmental changes through innovative composite designs and hybrid systems. This review provides a comprehensive examination of synthesis methods, functionalization techniques, and application pathways for materials derived from polysaccharides within the flexible electronics domain. It also addresses challenges related to scalability, stability, and regulatory considerations. Ultimately, this review illustrates how systems based on polysaccharides can bridge sustainability with technological advancement, establishing them as crucial materials for the creation of eco-friendly, high-performance, and commercially viable flexible electronic solutions. © 2025
Role of artificial intelligence in automating diagnostic procedures in clinical microbiology laboratories
(Elsevier B.V., 2025) Nishant Singhal; Harsh Vardhan; Rajul Jain; Payal Gupta; Aaysha Pandey; Naresh Kumar Wagri; Ashish Gaur
With infectious diseases continuing to pose a significant challenge to global health, clinical laboratories are pursuing faster, more accurate, and more scalable diagnostic options. This article highlights how advancements in robotics, machine learning, deep learning, and natural language processing are revolutionizing traditional laboratory practices. From automated Gram-staining and slide analysis to AI-enabled bacterial identification and antibiotic resistance testing, every technological development enhances diagnostic precision, reduces human error, and speeds up turnaround times. The assessment also deals with the real-world challenges of integrating these technologies, which include ethical issues, data privacy, system compatibility, and user acceptance. Additionally, it examines possible future developments, such as rapid diagnostics, smart laboratory infrastructure, and AI’s capability to create a seamless, interconnected network of diagnostic tools. As laboratories move towards completely automated and intelligent systems, combining human expertise with machine intelligence may enhance microbiological diagnostics’ quality, efficiency, and responsiveness in clinical settings. © 2025 The Author(s).
Starch–biomacromolecule complexes: A comprehensive review of interactions, functional materials, and applications in food, pharma, and packaging
(Elsevier Ltd, 2025) Harsh Vardhan; Nishant Singhal; Piyush Vashistha; Rajul Jain; Yograj Bist; Ashish Gaur; Naresh Kumar Wagri
Starch, a plentiful and biodegradable polysaccharide, has become a flexible platform material due to its renewability, affordability, and ability to improve functionality by complexing with biomacromolecules. Even with its inherent benefits, native starch faces drawbacks like low mechanical strength, high moisture susceptibility, and limited thermal stability, which impede its effectiveness in challenging applications. To address these limitations, starch is progressively blended with proteins, lipids, and polysaccharides, resulting in starch-biomacromolecule complexes (SBCs) that exhibit altered physicochemical and functional characteristics. These interactions-spanning hydrogen bonding, hydrophobic association, covalent crosslinking, and thermodynamic stabilization-enhance viscosity, gelation behavior, structural stability, and barrier properties. This analysis methodically explores the molecular processes involved in starch-biomacromolecule interactions, emphasizing how these complexes can be designed to customize functional attributes. It also consolidates recent progress in the use of SBCs in food systems (texture alteration, nutritional improvement, and preservation), pharmaceuticals (medicine delivery, controlled release, and biomedical frameworks), and packaging (biodegradable films, barrier layers, and active systems). New strategies like nano structuring, bioactive encapsulation, and hybrid composites are also thoroughly examined regarding their capability to tackle processing difficulties, environmental pressures, and scalability concerns. This article highlights the significance of SBCs as next-generation biomaterials for sustainable innovations in the food, health, and packaging sectors by connecting essential insights with technological applications. © 2025
Utilizing artificial intelligence and machine learning for enhanced recycling efforts
(IGI Global, 2025) Nikita Kandpal; Nishant Singhal; Harsh Vardhan Lavaniya; Rajul Jain; Rajat Singh; Ashish Gaur
One industry that has benefitted largely from the integration of Artificial Intelligence (AI) and machine learning (ML) in its processes is recycling, providing significant advancements in waste management towards sustainability and environmental conservation. This chapter highlights the application of AI and ML in various waste streams (plastic, electronic food, paper, textile, metal etc. wastage). These systems use AI-powered image recognition and sorting to better separate materials, helping in increasing the efficiency of chemical recycling technologies; meanwhile ML algorithms enable cleaner processes for handling chemicals from material recovery. Increased precision in the removal of valuable components from electronic waste via automated disassembly and predictive analytics. Using AI and ML in recycling has helped to increase operational efficiency, resources recovery but also shown clear contributions for the environment overall to ensure sustainable future ahead. © 2025, IGI Global Scientific Publishing. All rights reserved.