International Journal of AI and IoT Technologies (IJAIOT)
International Journal of AI and IoT Technologies (IJAIOT) is a double-blind peer-reviewed journal published Quarterly, four (4) Times a year, focusing on theories, methods, and applications in Artificial Intelligence (AI) and the Internet of Things (IoT). It provides a challenging forum for researchers, industrial professionals, engineers, managers, and policymakers working in the field to contribute and disseminate innovative new work on Artificial Intelligence (AI) and the Internet of Things (IoT). International Journal of AI and IoT Technologies (IJAIOT) Publisher: Academics Achievers Education And Research Foundation (AAERF), India Address: Umrapur, Ibrahimpur, District Raebareli, Pin-229212 Email: admin@academicsachievers.in Editor in Chief: Dr. Jaydip Kumar ISSN (Online): NA ISSN (Print): NA Manuscript Turnaround Time Editorial Review: 2-4 days. Time to First Decision (Peer Review Decision): 2–3 weeks Overall estimated time from submission to decision: 4-6 weeks. Revision Time: 1-2 Weeks Starting Year of the Journal: 2025 Frequency: Published quarterly every year in (February, May, August, and November). Publication Fees: Free If submitted within a reasonable timeframe of the issue, the journal’s review and publication process takes approximately 6–8 weeks. DOI: 10.70454/IJAIOT Coverage Areas: The topics covered by this journal include, but are not limited to, the following topics: 1. Artificial Intelligence (AI) and Machine Learning (ML) 2. Internet of Things (IoT) Systems & Applications 3. Embedded and Edge Computing 4. Smart Cities & Smart Environments 5. Neural Networks & Deep Learning 6. Automation & Robotics 7. Cyber-Physical Systems 8. Intelligent Communication Networks 9. Sensor Technologies Smart Devices 10. Secure IoT Architecture Protocols Types of Contributions: The Journal accepts the following categories of papers: Original Research Articles: Presenting novel research findings and contributions to the field. Review Papers: Providing comprehensive surveys of existing literature on specific topics.
Academics Achievers Education And Research Foundation
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Perovskite/Silicon Tandem Solar Cells: A Scalable Manufacturing Process Using Slot-Die Coating for Enhanced Photovoltaic Efficiency
The photovoltaic (PV) industry, dominated by single-junction crystalline silicon (c-Si) technology, is approaching its practical efficiency limit. To push beyond the Shockley-Queisser limit and further reduce the levelized cost of electricity (LCOE), perovskite/silicon tandem solar cells have emerged as a transformative technology. These devices leverage the complementary bandgaps of silicon (~1.1 eV) and perovskite (~1.6-1.8 eV) to more efficiently harvest the solar spectrum, with lab-scale devices now exceeding 33% efficiency. However, a critical bottleneck preventing their commercialization is the transfer of these high-performance devices from spin-coated, small-area lab curiosities to large scale, industrially viable modules. This work addresses this challenge by presenting a comprehensive manufacturing process centered on slot-die coating, a pre-industrial deposition technique, for the fabrication of the perovskite top cell. We detail the optimization of the ink formulation, coating parameters (meniscus stability, temperature, speed), and drying conditions for depositing high-quality, pin-hole free perovskite and charge transport layers over large areas. Furthermore, we integrate this process with a commercially textured silicon heterojunction (SHJ) bottom cell, addressing the critical challenge of coating conformally on rough surfaces. The resulting tandem devices, fabricated on 6-inch substrates, achieved a stabilized champion efficiency of 28.5% with minimal performance dispersion (<3% relative standard deviation) across the substrate, demonstrating exceptional uniformity. This performance represents one of the highest reported for a slot-die coated perovskite/silicon tandem cell. A techno-economic analysis indicates that this slot-die-based manufacturing flow can significantly reduce material waste and manufacturing costs compared to spin-coating and vacuum-based alternatives. This study provides a critical pathway for bridging the gap between lab-scale innovation and the gigawatt scale manufacturing of high-efficiency, low-cost tandem photovoltaics, marking a significant step toward their sustainable commercialization.
Copyright (c) 2025 DR. HAYDAR ALI (Author) https://creativecommons.org/licenses/by/4.0
Bio-Inspired Nanocellulose Aerogels for the Simultaneous Adsorption of Microplastics and Heavy Metals from Wastewater
The contamination of global water resources by complex mixtures of pollutants, notably microplastics (MPs) and heavy metals, poses a severe threat to ecosystems and human health. Current water treatment technologies often struggle with the simultaneous removal of these disparate contaminants due to their different physicochemical properties. Inspired by the hierarchical, multifunctional structures found in nature, this study presents the design, fabrication, and evaluation of a novel, multifunctional aerogel composed of TEMPO-oxidized cellulose nanofibers (TOCNF) and chitosan, engineered for the synergistic adsorption of both MPs and heavy metal ions. The three-dimensional (3D) porous network of the TOCNF aerogel provides an excellent scaffold for high-flow wastewater treatment, while the chitosan incorporation introduces protonated amine groups for efficient metal chelation. A key bio-inspired innovation is the functionalization of the aerogel's internal surface with tannic acid, a plant-derived polyphenol, which creates a high-affinity "capture layer" through hydrophobic and hydrogen-bonding interactions for MPs. The aerogel was characterized using SEM, BET, FTIR, and XPS, confirming its hierarchical micro/nanoporous structure, high specific surface area (≥ 152 m²/g), and successful functionalization. In batch adsorption experiments, the aerogel demonstrated exceptional removal efficiencies, achieving >99% for lead (Pb(II)) and cadmium (Cd(II)) ions at concentrations of 50 mg/L, and >95% for polyethylene (PE) and polypropylene (PP) microplastics (size range 50-500 µm) at concentrations of 100 mg/L. In a continuous flow column study simulating real wastewater conditions, the aerogel maintained high removal efficiency (>90% for both pollutant classes) for over 50 bed volumes. The adsorption mechanisms were thoroughly investigated, revealing ion exchange and complexation for metals and hydrophobic interaction for MPs. This work provides a sustainable, scalable, and highly effective strategy for tackling complex water pollution, leveraging bio-inspired design and renewable materials to advance next-generation water remediation technologies.
Copyright (c) 2025 Prof. Sudip Kumar Das (Author) https://creativecommons.org/licenses/by/4.0
A Closed-Loop Neuromodulation System Using Machine Learning for Real-Time Prediction and Suppression of Epileptic Seizures
Epilepsy is a debilitating neurological disorder affecting over 50 million people worldwide, characterized by recurrent, unprovoked seizures. A significant portion of patients are refractory to pharmacological treatment, necessitating alternative therapies. Closed-loop neuromodulation systems, which deliver electrical stimulation in response to detected seizure activity, represent a promising intervention. However, current state-of-the-art systems primarily react to seizures already in progress, often after clinical onset, limiting their therapeutic efficacy and ability to prevent debilitating symptoms. This paper presents the development and validation of a novel closed-loop neuromodulation system that leverages machine learning (ML) for the realtime prediction of impending epileptic seizures, enabling preemptive intervention. Our system architecture integrates long-term intracranial electroencephalography (iEEG) data acquisition, a cloud-based model training pipeline, and an implantable, low-power microcontroller unit (MCU) for real time inference. We engineered a compact convolutional neural network (CNN) model capable of extracting spatiotemporal features from multichannel iEEG to identify preictal (pre-seizure) states. The model was trained and validated on a large, multi-patient dataset from the NeuroVista and ETHZ iEEG archives, achieving a mean seizure prediction sensitivity of 91.2% with a false prediction rate of 0.12 per hour. The optimized model was deployed on a custom-designed hardware platform featuring ultra-low-power consumption for continuous monitoring. In a simulated real-time case study with retrospective data, the system successfully predicted 89% of seizures with an average warning time of 58.2 seconds prior to electrographic onset. Upon prediction, the system triggers a tailored, high-frequency stimulation protocol delivered via depth electrodes to the seizure focus, which suppressed 85% of impending seizures in an established rodent kainate model of epilepsy. This work demonstrates a significant paradigm shift from reactive to proactive neuromodulation. By integrating predictive ML models with efficient hardware, this system holds the potential to dramatically improve the quality of life for patients with drugresistant epilepsy, offering a path toward truly preventive neurotherapeutics.
Copyright (c) 2025 Dr. P. Brindha Devi (Author) https://creativecommons.org/licenses/by/4.0
Benchmarking the Performance of NISQ-Era Quantum Processors in Simulating Molecular Structures for Drug Discovery
The application of quantum computing to molecular simulation promises to revolutionize computational chemistry and drug discovery by providing exact solutions to the electronic Schrödinger equation, a task that is intractable for classical computers for large molecules. However, the current era of Noisy Intermediate-Scale Quantum (NISQ) hardware is characterized by processors with a limited number of qubits, high error rates, and short coherence times, posing significant challenges for practical quantum advantage. This study presents a comprehensive benchmarking framework to evaluate the performance of leading NISQ-era superconducting quantum processors from IBM and Rigetti in simulating fundamental molecular structures relevant to drug discovery. We focus on the variational quantum eigensolver (VQE) algorithm to compute the ground-state energy of a series of prototypical molecules—hydrogen (H₂), lithium hydride (LiH), and a prototypical drug fragment, formic acid (HCOOH)—each increasing in complexity and qubit requirement. Our benchmarking suite assesses not only the accuracy of the computed energy relative to the Full Configuration Interaction (FCI) baseline but also critical hardware-performance metrics including algorithm success probability, convergence stability, and required quantum volume. The results demonstrate that while small molecules like H₂ (4 qubits) can be simulated with reasonable accuracy (error < 5 kcal/mol) on current hardware, the simulation of LiH (12 qubits) and HCOOH (16+ qubits) is severely hampered by noise, with errors exceeding chemical accuracy (1.6 kcal/mol) by an order of magnitude. Furthermore, we quantify the exponential growth in the number of required two-qubit gates and the corresponding fidelity loss, identifying this as the primary bottleneck. Through randomized compilation and noise-aware mapping, we show a 40% reduction in error for the H₂ simulation but minimal improvement for larger molecules, underscoring the fundamental limitations of NISQ devices. This work provides a rigorous, quantitative assessment of the current capabilities and limitations of quantum hardware for quantum chemistry, establishing a clear benchmark for progress and suggesting that fault-tolerant quantum computing, rather than NISQ-era devices, will likely be necessary for impactful quantum-driven drug discovery.
Copyright (c) 2025 Dr. Sanjay Kumar (Author) https://creativecommons.org/licenses/by/4.0