Advanced Functional Materials, EarlyView.This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva +9 more
wiley +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
The Relationship Between Parenting Styles and Children's Prosocial Behavior: The Mediating Role of Children's Emotional Intelligence. [PDF]
Behav Sci (Basel)Zhang S, Wang P, Wang W, Su H, Zhang X.
europepmc +1 more source
Advanced Functional Materials, EarlyView.Multicolor optoelectronic synapses are realized by vertically integrating solution‐processed MoS2 thin‐film and SWCNT. The electronically disconnected but interactive MoS2 enables photon‐modulated remote doping, producing a bi‐directional photoresponse.
Jihyun Kim +8 more
wiley +1 more source
The longitudinal impact of emotional intelligence and psychological empowerment on work engagement among university administrators: a cross-lagged panel model approach. [PDF]
Front PsycholZhou L, Wang X.
europepmc +1 more source
Planning abilities of children aged 4 years and 9 months to 8 1/2 years: Effects of age, fluid intelligence and school type on performance in the Tower of London test [PDF]
, 2008 Leandro Fernandes Malloy‐Diniz +5 more
openalex +1 more source
Neuroenergetics and “General Intelligence”: A Systems Biology Perspective [PDF]
, 2020 Tobias Debatin
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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