Results 221 to 230 of about 212,144 (329)
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
Genomic Diversity of Avocado in the Morogoro Region and Southern Highlands of Tanzania. [PDF]
Cortés AJ, Hussein JM, Juma I.
europepmc +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Genomic characterization of antimicrobial resistance and virulence determinants in <i>Salmonella</i> Infantis isolated from human, food, and animal sources. [PDF]
Han J, Tang E, Zhao S, Foley SL.
europepmc +1 more source
Phylogenetic Minimum Spanning Tree Reconstruction Using Autoencoders
Riccardo Castelletto +2 more
openalex +2 more sources
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
Dataset on stand structure in hemiboreal forests across management histories and stand ages. [PDF]
Rosenvald R +8 more
europepmc +1 more source
HAMSTER: visualizing microarray experiments as a set of minimum spanning trees. [PDF]
Wan R +4 more
europepmc +1 more source
Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis
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

