Results 141 to 150 of about 25,341,143 (317)
Advanced Intelligent Systems, EarlyView.Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai +10 more
wiley +1 more source
Machine Learning Accelerates Crystallization for Structure Determination
Angewandte Chemie, EarlyView.Single‐crystal x‐ray diffraction (SCXRD) is often constrained by the difficulty of obtaining suitable crystals. Here, a machine learning‐accelerated co‐crystal discovery workflow is established for a crystalline mate strategy that achieves over 95% prediction accuracy and experimentally delivers 114 co‐crystals from 120 candidates.
Cui‐Zhou Luan +10 more
wiley +2 more sources
Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis
Advanced Intelligent Systems, EarlyView.A machine learning framework is applied to photoluminescence spectra to extract linewidths and uncover how NV, SiV, GeV, and SnV centers evolve with growth and processing conditions. Unified normalization and k‐fold validation reveal cross‐method trends and enable rapid prediction of defect size and fabrication parameters, offering a data‐driven route ...
Zhi Jiang +3 more
wiley +1 more source
Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry
Angewandte Chemie, EarlyView.A high‐throughput direct infusion mass spectrometry platform, enabled by gas‐phase ion mobility separation, supports single‐injection analysis of peptides, polar metabolites, and lipids. Coupled with custom software, it identified ∽1,300 proteins and ∽600 metabolites in ∽4.3 minutes per sample, and demonstrated broad utility in macrophage polarization ...
Yuming Jiang +6 more
wiley +2 more sources
Advanced Intelligent Systems, EarlyView.This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.A statistical and machine learning‐assisted surface‐enhanced Raman scattering (SERS) framework is developed for label‐free quantification of low‐abundance analytes, including proteins. Combining digital SERS event counting with binomial regression and an artificial neural network (ANN) trained on full spectra, the approach achieves picomolar detection ...
Eni Kume, James Rice
wiley +1 more source
Angewandte Chemie, EarlyView.Dual‐atom catalysts (DACs) for oxygen reduction reaction (ORR), where the dissociative mechanism dominates, show a dual‐Sabatier optima volcano map, diverging from the classical single‐peak volcano of the associative mechanism. Potential‐dependent microkinetic modeling and interpretable machine learning rationalize experimental data and provide design ...
Jin Liu +3 more
wiley +2 more sources
Advanced Intelligent Systems, EarlyView.An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna +2 more
wiley +1 more source
Chronological Diagnostic Algorithm Predicting Neuropathology in Parkinsonism
Annals of Neurology, EarlyView.Objective Pre‐mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co‐pathologies. This study aimed to develop a machine learning‐based algorithm predicting neuropathology in parkinsonism using chronological clinical presentations, which has previously been underexplored.
Daisuke Ono +5 more
wiley +1 more source
Annals of Neurology, EarlyView.Objective The objective of this study was to examine whether machine learning has the capacity to prospectively identify and predict the emergence of Fragile X‐associated tremor/ataxia syndrome (FXTAS) among male fragile X premutation carriers (PCs). Methods We explored neuropsychological and motor evaluation metrics, brain magnetic resonance imaging ...
Chitrabhanu Gupta +10 more
wiley +1 more source