Integration of AI-Based Nano Synergy in Bayesian Uncertainty Quantification for Advanced Engineering Design DOI Open Access
S. N. Deepa,

Dr.Meenakshipatil,

Padmini Kaji

et al.

Nanotechnology Perceptions, Journal Year: 2024, Volume and Issue: unknown, P. 77 - 89

Published: Dec. 1, 2024

The advancement in artificial intelligence and nanotechnology has provided new solutions for tackling problems enhanced engineering design. This research focuses on both AI assisted observational methodologies Bayesian uncertainty quantification (BUQ) improving the predictive models, material properties, design procedures. Four complex techniques of estimating managing are following: Neural Networks (BNN), Gaussian Processes (GP), Monte Carlo Dropout (MCD), Ensemble Learning (EL). Numerical studies revealed that forecast accuracy proposed framework is 94.6% with BNN 93.1% GP, which makes excellent improvements over prior arts up to 15% quantification. Besides, computational resources less by 20% EL compared standalone approaches, while incorporation nanoscale information increase AT RT 17%. To demonstrate AI-driven BUQ addresses limitations existing a comparative discussion provided. results reinforce its viability providing sustainable efficient under conditions risk. work may be used as platform subsequent synergies between AI, nanotechnology, advanced materials systems drive progress well

Language: Английский

Recent advances in medical gas sensing with artificial intelligence–enabled technology DOI Creative Commons
Chitaranjan Mahapatra

Medical Gas Research, Journal Year: 2025, Volume and Issue: 15(2), P. 318 - 326

Published: Jan. 18, 2025

Recent advancements in artificial intelligence–enabled medical gas sensing have led to enhanced accuracy, safety, and efficiency healthcare. Medical gases, including oxygen, nitrous oxide, carbon dioxide, are essential for various treatments but pose health risks if improperly managed. This review highlights the integration of intelligence sensing, enhancing traditional sensors through advanced data processing, pattern recognition, real-time monitoring capabilities. Artificial improves ability detect harmful levels, enabling immediate intervention prevent adverse effects. Moreover, developments nanotechnology resulted materials, such as metal oxides carbon-based nanomaterials, which increase sensitivity selectivity. These innovations, combined with intelligence, support continuous patient predictive diagnostics, paving way future breakthroughs care.

Language: Английский

Citations

2

Biochar-based electrochemical sensors: a tailored approach to environmental monitoring DOI Open Access
Alvin Lim Teik Zheng,

Ellie Teo Yi Lih,

Pang Hung Yiu

et al.

Analytical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Biochar (BC), often obtained via thermochemical conversion methods of biomass, has emerged as a versatile material with significant potential in electrochemical sensing applications. This review critically examines the recent advancements development BC-based sensors for determination pharmaceuticals, pesticides, heavy metals, phenolic compounds, and microplastics. have promising alternative due to their sustainability, cost-effectiveness, excellent properties. The unique physicochemical properties BC, including its high surface area, porosity, functional groups, contribute effectiveness sensor material. begins an overview synthesis highlighting activation strategies on structural Next, functionalization BC integration into platforms are explored. performance is evaluated using focusing sensitivity, selectivity, detection limits, stability. Future directions research proposed, emphasizing need further optimization, miniaturization, portable on-site analytical devices.

Language: Английский

Citations

2

Impact of Metabolites from Foodborne Pathogens on Cancer DOI Creative Commons
Alice Njolke Mafe, Dietrich Büsselberg

Foods, Journal Year: 2024, Volume and Issue: 13(23), P. 3886 - 3886

Published: Dec. 1, 2024

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins secondary metabolites, their implications for human health, particularly concerning cancer risk. We examine various such as Salmonella sp., Campylobacter Escherichia coli, Listeria monocytogenes, detailing specific of concern carcinogenic mechanisms. study discusses analytical techniques detecting chromatography, spectrometry, immunoassays, along with challenges associated detection. covers effective control strategies, processing techniques, sanitation practices, regulatory measures, emerging technologies in pathogen control. manuscript considers broader highlighting importance robust policies, awareness, education. identifies research gaps innovative approaches, recommending advancements detection methods, preventive policy improvements better manage foodborne metabolites.

Language: Английский

Citations

10

Silicon-Based Biosensors: A Critical Review of Silicon’s Role in Enhancing Biosensing Performance DOI Creative Commons
Waqar Muhammad, Jaeyoon Song, Sehyeon Kim

et al.

Biosensors, Journal Year: 2025, Volume and Issue: 15(2), P. 119 - 119

Published: Feb. 18, 2025

This review into recent advancements in silicon-based technology, with a particular emphasis on the biomedical applications of silicon sensors. Owing to their diminutive size, high sensitivity, and intrinsic compatibility electronic systems, sensors have found widespread utilization across healthcare, industrial, environmental monitoring domains. In realm sensing, has demonstrated significant potential enhance human health outcomes while simultaneously driving progress microfabrication techniques for multifunctional device development. The systematically examines versatile roles fabrication electrodes, sensing channels, substrates. Silicon electrodes are widely used electrochemical biosensors glucose neural activity recording, channels field-effect transistor enable detection cancer biomarkers small molecules. Porous substrates applied optical label-free protein pathogen detection. Key challenges this field, including interaction biomolecules, economic barriers miniaturization, issues related signal stability, critically analyzed. Proposed strategies address these improve sensor functionality reliability also discussed. Furthermore, article explores emerging developments biosensors, particularly integration wearable technologies. pivotal role artificial intelligence (AI) enhancing performance, functionality, real-time capabilities is highlighted. provides comprehensive overview current state, challenges, future directions field

Language: Английский

Citations

1

Decentralized electrochemical biosensors for biomedical applications: From lab to home DOI Creative Commons
Pramod K. Kalambate, Vipin Kumar,

Dhanjai Dhanjai

et al.

Next Nanotechnology, Journal Year: 2025, Volume and Issue: 7, P. 100128 - 100128

Published: Jan. 1, 2025

Language: Английский

Citations

0

Innovative integration of machine learning and colorimetry for precise potential of hydrogen monitoring in printed hydrogel sensors DOI Creative Commons
Abdelrahman Sakr, Ahmed Elshamy, Haider Butt

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 146, P. 110293 - 110293

Published: Feb. 18, 2025

Language: Английский

Citations

0

Trends and Advances in Wearable Plasmonic Sensors Utilizing Surface-Enhanced Raman Spectroscopy (SERS): A Comprehensive Review DOI Creative Commons
Svetlana N. Khonina, Nikolay L. Kazanskiy

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1367 - 1367

Published: Feb. 23, 2025

Wearable sensors have appeared as a promising solution for real-time, non-invasive monitoring in diverse fields, including healthcare, environmental sensing, and wearable electronics. Surface-enhanced Raman spectroscopy (SERS)-based leverage the unique properties of SERS, such plasmonic signal enhancement, high molecular specificity, potential single-molecule detection, to detect identify wide range analytes with ultra-high sensitivity selectivity. However, it is important note that utilize various sensing mechanisms, not all rely on SERS technology, their design depends specific application. This comprehensive review highlights recent trends advancements technologies, focusing design, fabrication, integration into practical devices. Key innovations material selection, use nanomaterials flexible substrates, significantly enhanced sensor performance wearability. Moreover, we discuss challenges miniaturization, power consumption, long-term stability, along solutions address these issues. Finally, outlook technologies presented, emphasizing need interdisciplinary research drive next generation smart wearables capable real-time health diagnostics, monitoring, beyond.

Language: Английский

Citations

0

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry DOI

Jaleh Bagheri Hamzyan Olia,

Arasu Raman, Chou‐Yi Hsu

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109984 - 109984

Published: March 14, 2025

Language: Английский

Citations

0

Electrochemically active DNA ligands for gene detection: present and future DOI Open Access
Shigeori Takenaka, Shinobu Sato

Analytical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Electrochemical gene sensing methods are gaining attention as diagnostic chips. Here, we review the electrochemically active DNA ligand-based methods. Various ligands have been reported in these studies, among which metal complexes, methylene blue, and ferrocenyl naphthalene diimide (FND) studied detail. probe immobilized electrodes created, hybridization reactions on with target fragments performed, electrochemical detection has possible using ligands. An example of realization this system is successful accurate cancer diagnosis FND to examine abnormal methylation hTERT gene, providing reassurance about system's reliability. In addition, PCR products realized current decrease due double-stranded binding blue although it a signal-off system. A derivative ferrocene β-CD, FNC, increased upon binding. Using FNCs, homogeneous was realized. qPCR Since FNDs also bind strongly tetraplex or G-quadruplex (G4) DNA, succeeded detecting telomerase activity, known marker, detect amount telomeric elongation, its substrate, G4 DNA. This technique compassionate from oral swab fluid. It that present viral genome RNA, testing method expected be potential alternative PCR. The first novel coronaviruses incFND an RNA ligand.

Language: Английский

Citations

0

Machine Learning–Based Calibration of Commercial Continuous Glucose Monitoring Sensor in Nonserum Solutions: An In Vitro Validation Study DOI Creative Commons
Megha Gautam, Aditya Choudhary, Deepak Agrawal

et al.

Indian Journal of Neurotrauma, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Abstract Continuous glucose monitoring (CGM) systems, such as the FreeStyle Libre Pro (Abbott Diabetes Care), offer noninvasive measurement. However, their accuracy in cerebrospinal fluid (CSF) remains unvalidated. This study evaluates performance of sensor against a standard laboratory analyzer and proposes regression-based calibration model to enhance measurement neurotrauma ICU. A was integrated into an experimental setup using adapter. Sensor readings were recorded with concentrations ranging from 50 275 mg/dL. used reference. linear regression trained correct deviations, interpolation (SciPy's interp1d) for refined predictions. Real-time data acquisition facilitated via Universal asynchronous receiver / transmitter (UART)-based serial communication, adaptive learning enabled retraining upon accumulating 10 value pairs. Initial exhibited significant deviations values, particularly at lower (mean absolute relative difference [MARD]: 30.45%). Postcalibration, MARD reduced 8.92%, demonstrating improved accuracy. Interpolation further minimized correcting values 40 mg/dL (20% deviation) 49.1 (1.8% 72 (42.4% 123.5 (1.2% deviation). Adaptive progressively root mean square error (RMSE) 23.7 9.8 after 30 updates. The makes more accurate CSF measurements. method might be promising continuously patients external ventricular drainage, improving patient care

Language: Английский

Citations

0