Results 191 to 200 of about 4,096,407 (285)
A computational chemistry-based approach to optimizing PD-1/PD-L1 inhibitors. [PDF]
Zhai M +7 more
europepmc +1 more source
Edible electronics needs integrated logic circuits for computation and control. This work presents a potentially edible printed chitosan‐gated transistor with a design optimized for integration in circuits. Its implementation in integrated logic gates and circuits operating at low voltage (0.7 V) is demonstrated, as well as the compatibility with an ...
Giulia Coco +8 more
wiley +1 more source
Hamiltonian Computational Chemistry: Geometrical Structures in Chemical Dynamics and Kinetics. [PDF]
Farantos SC.
europepmc +1 more source
ABSTRACT Chitosan, a polysaccharide upcycled from biowaste, has long promised sustainable, biocompatible, and antimicrobial films and devices, an appeal reinforced by its recent regulatory recognition, with several chitosan‐based antibacterial dressings gaining clearance through the Food and Drug Administration's 510(k) pathway.
Jacopo Nicoletti +16 more
wiley +1 more source
Verification of the interaction between human bitter taste receptor T2R46 and polyphenols; Computational chemistry approach. [PDF]
Shimizu T +9 more
europepmc +1 more source
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
Harnessing thiazole chemistry for antifungal strategies through an experimental and computational chemistry approach: anti-biofilm, molecular docking, dynamics, and DFT analysis. [PDF]
Shinde RA +12 more
europepmc +1 more source
Implantable Ionic Memristors Based on Natural Polymer Heterojunctions
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee +6 more
wiley +1 more source

