Results 231 to 240 of about 695,964 (314)
Advanced Functional Materials, EarlyView.The separation of Helium gas from natural gas is challenging but highly important. MIL‐116(Ga), a “non‐porous” metal–organic framework is used as a molecular sieve to separate He from CH4. Druse‐like MIL‐116(Ga) particles are integrated into polysulfone mixed matrix membranes.
Ayisha Komal +10 more
wiley +1 more source
Effects of Shading on Metabolism and Grain Yield of Irrigated Rice During Crop Development. [PDF]
Plants (Basel)Pires SN +6 more
europepmc +1 more source
Laser‐Induced Graphene from Waste Almond Shells
Advanced Functional Materials, EarlyView.Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova +9 more
wiley +1 more source
Genome-wide association analysis to identify QTLs and candidate genes associated with grain yield and its related traits under low light conditions in rice (Oryza sativa L.). [PDF]
Plant GenomeDas S +15 more
europepmc +1 more source
Advanced Functional Materials, EarlyView.Controlled syntheses of lanthanide coordination polymers based on the dihydroxybenzoquinone (DHBQ) organic linker afforded large single crystals of Ln‐DHBQ CPs (Ln = Yb, Nd). A novel structural variant of Yb‐DHBQ is identified by means of single crystal diffraction analysis.
Marina I. Schönherr +7 more
wiley +1 more source
Effect of Plant Topping on Seasonal Development, Physiological Changes, and Grain Yield of Soybean. [PDF]
Plants (Basel)Lee S +5 more
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Advanced 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
Genetic association analysis of grain yield-related traits in Aegilops tauschii under drought and non-stress conditions. [PDF]
Sci RepFalaknaz M +4 more
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced 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