AIMC Topic: Electrochemical Techniques

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Machine Learning for Separating Dopamine and Octopamine Electrochemical Signals in Drosophila.

Analytical chemistry
, the fruit fly, uses the neurotransmitters dopamine and octopamine to mediate learning, enabling adaptive behaviors such as reward seeking and punishment avoidance. Their colocalization in the mushroom bodies makes it challenging to study their indi...

Adsorption Energy Prediction Model for CO Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements.

Journal of chemical information and modeling
Electrochemical carbon dioxide reduction (CORR) using electrocatalysts has gained attention for its potential to convert atmospheric CO into value-added chemicals. Recently, machine learning (ML) has emerged as a promising approach for catalyst devel...

Deep Learning-Assisted G4 Nanowire-Enhanced Carbon Dot Biosensor for Exosomal LncRNA Artificial Intelligence Diagnosis.

Analytical chemistry
Exosomal long noncoding RNAs (lncRNA) have significant potential as a biomarker for early cancer diagnosis. Accurate and sensitive detection of this abnormal expression remains challenging. Herein, we develop an innovative dual-mode photoelectrochemi...

Machine Learning for Neurotransmitter Monitoring by Fast Voltammetry: Current and Future Prospects.

ACS chemical neuroscience
Chemical neuroscience wields tools to uncover the molecular mysteries of the brain. Sensors can be fabricated with properties tailored to the scales needed to decode neurochemical information. Current instrumentation is capable of measurement rates t...

Overcoming Challenges in Electrochemical Sensing: Toward Continuous Monitoring.

ACS sensors
The advancement of decentralized and real-time monitoring necessitates robust electrochemical sensors that can operate continuously in complex environments. However, transitioning these sensors from laboratory prototypes to reliable field devices rem...

Machine learning-integrated electrochemical sensing of ciprofloxacin for digital point-of-care therapeutic drug monitoring.

Mikrochimica acta
Timely and precise therapeutic drug monitoring (TDM) is critical for managing pharmacokinetic variability and optimizing individualized therapy, particularly during public health crises such as the COVID-19 pandemic. Herein, we optimized integrated m...

From Lab to Body: Advanced Electrochemical Biosensors for Illicit Drug Detection via Nanomaterials, AI, and Wearable Tech.

ACS sensors
Illicit drug detection is entering a transformative era, driven by the convergence of electrochemical sensing, nanomaterials engineering, and artificial intelligence. Traditional analytical approaches, despite their precision, are increasingly misali...

Reticular-Induced Energy Transfer Driven Renewable ECL System with Machine Learning for Glioma-Specific Dual-Biomarker Detection and Expression Correlation Mechanism.

Analytical chemistry
Rapid, accurate, and renewable electrochemiluminescence (ECL) bioassays are crucial for multiplexed biomarker detection. Integrated with efficient analytical model for processing sensing data, these tools enable precise differentiation of tumor stage...

AI-Assisted Microfluidic Paper-Based Analytical Device with Au-Pt Nanoparticles for Multiplex, Interference-Resistant Quantification of Urinary Biomarkers.

Analytical chemistry
Urinary glucose, creatinine, and uric acid are vital biomarkers for diabetes and kidney disease management. However, multiplex point-of-care detection faces challenges due to insufficient sensitivity in complex urine matrices and signal cross-talk fr...

Durative Monitoring of Sulfur Hexafluoride Characteristic Gases under Hydrogen Interference Using a Time2Vec-Encoded CNN-Transformer-LSTM Model Based on a Heterogeneous Gas Sensor Array.

ACS sensors
Gas-insulated switchgear (GIS) systems extensively employ sulfur hexafluoride (SF) as an insulating medium and are widely deployed in modern power systems. Under partial discharge (PD) conditions, SF decomposes to generate hazardous byproducts such a...