Results 191 to 200 of about 2,150,687 (292)

Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction. [PDF]

open access: yesPlant Genome
Lubanga N   +10 more
europepmc   +1 more source

Positive‐Tone Nanolithography of Antimony Trisulfide with Femtosecond Laser Wet‐Etching

open access: yesAdvanced Functional Materials, EarlyView.
A butyldithiocarbamic acid (BDCA) etchant is used to fabricate various micro‐ and nanoscale structures on amorphous antimony trisulfide (a‐Sb2S3) thin film via femtosecond laser etching. Numerical analysis and experimental results elucidate the patterning mechanism on gold (reflective) and quartz (transmissive) substrates.
Abhrodeep Dey   +12 more
wiley   +1 more source

Prediction accuracy for feed intake and body weight gain using host genomic and rumen metagenomic data in beef cattle. [PDF]

open access: yesGenet Sel Evol
Lakamp A   +8 more
europepmc   +1 more source

Band Alignment in In‐Oxo Metal Porphyrin SURMOF Heterojunctions

open access: yesAdvanced Functional Materials, EarlyView.
Porphyrin core metalation in indium‑oxo SURMOFs enables systematic tuning of band edge positions without altering the crystal structure. First‑principles calculations reveal type‑I and type‑II heterostructures as well as multi‑junction energy cascades, establishing a modular strategy for exciton funneling and charge separation in optoelectronic ...
Puja Singhvi, Nina Vankova, Thomas Heine
wiley   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

open access: yesAdvanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore   +7 more
wiley   +1 more source

Improving T2D machine learning-based prediction accuracy with SNPs and younger age. [PDF]

open access: yesComput Struct Biotechnol J
Hageh CA   +10 more
europepmc   +1 more source

Golden‐Ratio–Guided Aperiodic Architected Metamaterials with Simultaneously Enhanced Strength and Toughness

open access: yesAdvanced Functional Materials, EarlyView.
Guided by the golden ratio, a class of aperiodic architected metamaterials is introduced to address the intrinsic trade‐off between strength and toughness. By unifying local geometric heterogeneity with global order, the golden‐ratio‐guided aperiodic architecture promotes spatial delocalization of damage tolerence regions, leading to more tortuous ...
Junjie Deng   +9 more
wiley   +1 more source

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