Differential grain quality responses of rice varieties under combined salt, cadmium and arsenic stresses. [PDF]
Rifasa S +7 more
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
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
Genetic Diversity of Prolamin Loci Related to Grain Quality in Durum Wheat (<i>Triticum durum</i> Desf.) in Kazakhstan. [PDF]
Utebayev M +11 more
europepmc +1 more source
Impact of tillage on yield and quality traits of grains of spring wheat cultivars
Alicja Sułek +2 more
openalex +1 more source
A bespoke multilayer thin film configuration has been designed, which overcomes the material dependency of conventional isotope exchange Raman spectroscopy (IERS). This universal IERS methodology is efficient, non‐destructive and provides additional structural information and time resolution, which can be further extended to various isotopic elements ...
Zonghao Shen +7 more
wiley +1 more source
Effects of Microwave on Mortality and Detection Efficiency of Three Stored Grain Insect Adults in Stored Paddy, and on Grain Quality. [PDF]
Miao S +6 more
europepmc +1 more source
Effects of different timing and rate of glyphosate application on the residue level, grain quality, and processing performance of two Canadian malting barley varieties [PDF]
Marta S. Izydorczyk +4 more
openalex +1 more source
Effects of Foliar Application of Nitrogen, Zinc and Manganese on Yield, Yield Components and Grain Quality of Chickpea in Two Growing Seasons [PDF]
Babak Shirani +3 more
openalex +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
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
CRISPR/Cas9-mediated inactivation of the soybean agglutinin <i>Le1</i> gene to improve grain quality. [PDF]
Kafer JM +8 more
europepmc +1 more source

