Optimization of conductivity for MXene polymer nanocomposites by the number and diameter of contacts among adjacent nanosheets - Nature
This story deserves to be shared
Send it to someone who'd care, or post it on your networks.
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.
This article is an excerpt. To read the full content, visit the original source. Nature
Related articles

Machine learning modeling and interpretation of saline soil thermal conductivity - Nature
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.
Integrating high-resolution magnetic surveys and metaheuristic optimization for quantitative characterization of subsurface magnetic sources in a municipal landfill - Nature
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. AbstractLandfill leachate poses a significant environmental risk by transporting toxic metals and organic contaminants into surrounding soil and groundwater. This study establishes a rapid, non-invasive geophysical framework for delineating potential subsurface leachate pathways using high-resolution magnetic surveying integrated with advanced magnetic data processing and metaheuristic optimization. A total of 246 magnetic measurements were acquired across the Benowo landfill, Surabaya, Indonesia, using a proton precession magnetometer. The data were processed through diurnal correction, International Geomagnetic Reference Field (IGRF) removal, upward continuation, Reduction-to-the-Pole (RTP) transformation, analytic signal, first vertical gradient, and horizontal gradient analyses to enhance anomaly localization and source-boundary delineation.
Integrating hippopotamus optimization algorithm into machine learning frameworks for predicting suspended sediment concentrations - Nature
ORCID: orcid.org/0000-0001-8061-88361, Yaser Mehri2, Ali Abdolrazaq Sabbar3, Mohammad Javad Nahvinia3 & … Mohammad Hosein Omid2 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.
0 comments
No comments yet. Be the first to share your thoughts!