ArticleSmall (Weinheim an der Bergstrasse, Germany)2026
A Structurally Robust Framework for Intelligent Graphene Thermometry via Few-Shot Transfer Learning and Algorithm-Hardware Co-Design.
Article in Small (Weinheim an der Bergstrasse, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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11 authors.
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Abstract
While ultrathin graphene sensors hold promise for flexible electronics, their applicability faces a challenging arising from the unavoidable nanoscale stochasticity and structural variability of compliant substrates. Features like intrinsic grain boundaries and microscopic wrinkles inevitably create pronounced device-to-device heterogeneity, rendering traditional batch calibration unreliable for high-precision applications. Rather than eliminating these inherent physical imperfections, we introduce a variability-resilient sensing framework built on an algorithm-hardware co-design. By employing a few-shot transfer learning architecture with a frozen-backbone neural network, our system effectively learns the universal physics of graphene carrier scattering from a source array and can rapidly adapt to the unique electrical footprint of new, uncalibrated devices using less than 1% of conventional calibration data (R
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.