Results 271 to 280 of about 1,869,696 (352)

Engineered Protein‐Based Ionic Conductors for Sustainable Energy Storage Applications

open access: yesAdvanced Materials, EarlyView.
Rational incorporation of charged residues into an engineered, self‐assembling protein scaffold yields solid‐state protein films with outstanding ionic conductivity. Salt‐doping further enhances conductivity, an effect amplified in the engineered variants. These properties enable the material integration into an efficient supercapacitor.
Juan David Cortés‐Ossa   +14 more
wiley   +1 more source

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification. [PDF]

open access: yesBioinformatics
Pancotti C   +5 more
europepmc   +1 more source

Mutational Signature Analysis Reveals NTHL1 Deficiency to Cause a Multi-tumor Phenotype.

open access: yesCancer Cell, 2019
Judith E. Grolleman   +54 more
semanticscholar   +1 more source

Kelvin Probe Force Microscopy in Bionanotechnology: Current Advances and Future Perspectives

open access: yesAdvanced Materials, EarlyView.
Kelvin probe force microscopy (KPFM) enables the nanoscale mapping of electrostatic surface potentials. While widely applied in materials science, its use in biological systems remains emerging. This review presents recent advances in KPFM applied to biological samples and provides a critical perspective on current limitations and future directions for
Ehsan Rahimi   +4 more
wiley   +1 more source

A multi-looping chromatin signature predicts dysregulated gene expression in neurons with familial Alzheimer’s disease mutations [PDF]

open access: gold
Harshini Chandrashekar   +9 more
openalex   +1 more source

Stable expansion of high-grade serous ovarian cancer organoids requires a low-Wnt environment

open access: yes, 2020
Berger, H.   +13 more
core   +1 more source

Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization

open access: yesAdvanced Materials Technologies, EarlyView.
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue   +8 more
wiley   +1 more source

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