This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrix engineered for stable electrical conductivity and structural integrity across extreme thermal ranges. The system integrates ultra-high-temperature ceramics, cryogen

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrixengineered for stable electrical conductivity and structural integrity across extreme thermal ranges.The system integrates ultra-high-temperature ceramics, cryogenic conductive structures, gradedneutral buffer layers, and embedded AI-driven sensing architecture. Theoretical modeling, thermalcycle simulations (1000 day-night cycles), regression-based predictive stability modeling, andstructural compounding methods are presented. The framework demonstrates feasibility foraerospace, energy, defense, cryogenic infrastructure, and high-temperature industrial applications.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-16
DOI
https://doi.org/10.5281/zenodo.18666903
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrix engineered for stable electrical conductivity and structural integrity across extreme thermal ranges. The system integrates ultra-high-temperature ceramics, cryogen

PRABIN KUMAR
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrix engineered for stable electrical conductivity and structural integrity across extreme thermal ranges. The system integrates ultra-high-temperature ceramics, cryogen

PRABIN KUMAR
article en

Abstract

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrixengineered for stable electrical conductivity and structural integrity across extreme thermal ranges.The system integrates ultra-high-temperature ceramics, cryogenic conductive structures, gradedneutral buffer layers, and embedded AI-driven sensing architecture. Theoretical modeling, thermalcycle simulations (1000 day-night cycles), regression-based predictive stability modeling, andstructural compounding methods are presented. The framework demonstrates feasibility foraerospace, energy, defense, cryogenic infrastructure, and high-temperature industrial applications.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 54%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrix engineered for stable electrical conductivity and structural integrity across extreme thermal ranges. The system integrates ultra-high-temperature ceramics, cryogen — PRABIN KUMAR · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS