Microplastics from Wearable Bioelectronic Devices: Sources, Risks, and Sustainable Solutions
Bioelectronic devices (e.g., e‐skins) heavily rely on polymers that at the end of their life cycle will generate microplastics. For research, a holistic approach to viewing the full impact of such devices cannot be overlooked. The potential for devices as sources for microplastics is raised, with mitigation strategies surrounding polysaccharide and ...
Conor S. Boland
wiley +1 more source
A Learning Framework for Atomic-Level Polymer Structure Generation. [PDF]
Jain A, Srivastava A, Ramprasad R.
europepmc +1 more source
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Tandem Mass Spectrometry Reflects Architectural Differences in Analogous, Bis-MPA-Based Linear Polymers, Hyperbranched Polymers, and Dendrimers. [PDF]
Williams-Pavlantos K +5 more
europepmc +1 more source
Integrative Approaches for DNA Sequence‐Controlled Functional Materials
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo +4 more
wiley +1 more source
Mechanoluminescence from amorphous solids of heteroleptic copper complexes and common luminophores induced by non-destructive mechanical stimuli and fabrication of flexible mechanoluminescent films. [PDF]
Karimata A +6 more
europepmc +1 more source
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Controlled Copolymerization of Ethylene and Biosourced Comonomers Using Dibenzobarrelene-Based <i>α</i>-Diimine Nickel Catalyst. [PDF]
Zheng H +6 more
europepmc +1 more source

