Intercalated carbon nanotube fibers with high specific electrical conductivity - Science | AAAS

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractIn this study, a model for the conductivity of MXene-based samples is developed by simultaneously considering the percolation threshold, inter-nanosheet interfacial contacts, contact geometry, and electron tunneling effects. The percolation onset volume fraction and the network portion are demonstrated by interphase dimensions as well as the diameter and thickness of the MXene nanosheets. Accordingly, the proposed model is constructed based on physically meaningful and measurable parameters, providing an efficient framework for estimating the conductivity of MXene system. Parametric analysis and experimental conductivity facts from representative systems are employed to validate the method. The highest conductivity as 5.1 S/m is gained when nearly all MXene nanosheets participate in the conductive network and the interphase thickness is 40 nm.

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractSoil thermal conductivity is a key parameter in geothermal design, buried infrastructure, frozen-ground engineering, agricultural heat-transfer assessment, and subsurface energy systems. This study developed an interpretable multi-model machine-learning framework for predicting soil thermal conductivity from 903 literature-derived observations and 15 predictors describing temperature, texture, bulk density, volumetric water content, salt concentration, salt molar mass, and ionic identity. Eleven regression algorithms were optimized by five-fold cross-validation within a common 90% development subset and compared on an untouched 10% random hold-out. Extra Trees yielded the lowest observed random-holdout error (R² = 0.9816, RMSE = 0.0625 W m⁻¹ K⁻¹, MAE = 0.0446 W m⁻¹ K⁻¹, AARE = 3.93%, and A20 = 98.90%), with CatBoost and XGBoost providing similarly high point performance.

Cells constantly recycle proteins and other cellular components. But how do they ensure that the right cargo ends up in the right recycling vesicle—one of the cell's transport carriers? A team at the University of Basel has identified a molecular switch that controls this process. The small protein ARF-1 ensures that recyclable cargo is efficiently loaded into transport vesicles and returned to the cell's surface.