Evidence map›Paper›PMID 42531602›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Substrate Engineering of SWCNT p-n Junctions for Dual-Mode Power Generation and Heat-Flux Sensing.

Ryota Tamai, Hiroto Nakayama, Shuya Ochiai, Masayuki Takashiri

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, 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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ryota TamaiDepartment of Materials Science, Tokai University, Hiratsuka, Japan.
Hiroto NakayamaDepartment of Materials Science, Tokai University, Hiratsuka, Japan.
Shuya OchiaiDepartment of Materials Science, Tokai University, Hiratsuka, Japan.
Masayuki TakashiriDepartment of Materials Science, Tokai University, Hiratsuka, Japan.ORCID https://orcid.org/0000-0001-7041-6595

Funding

Kanto Yakin Kogyo Co., Ltd.
6 · The paper itself

Abstract

Autonomous thermoelectric power generation under uniform heating, without an external cold reservoir, remains an open challenge for self-powered Internet-of-Things (IoT) sensors. This work shows that, for a fixed single-walled carbon nanotube (SWCNT) p-n junction film, circuit, and adhesive-bonded device structure, the substrate alone produces a substrate-dependent transition between continuous power generation and high-sensitivity heat-flux sensing. Across three flexible substrates, cycloolefin polymer (COP), polyimide (PI), and polyethylene naphthalate (PEN), substrate infrared absorptivity and per-unit-area thermal inertia jointly govern the sign, magnitude, and temporal evolution of the output voltage. COP (absorbance 0.34; transmittance 45.8% at 8.8 µm) heats the film preferentially, sustaining a stable in-plane gradient and delivering +0.38 mV at steady state. PI and PEN, with near-complete absorption, transiently invert the gradient, producing excursions of -0.58 and -0.96 mV; the larger PEN response reflects 2.4-fold greater thermal inertia, yielding -45 µV/K per junction pair. A transient thermal model and thermographic imaging reproduce these dynamics and indicate that the substrate optical contrast, rather than the interfacial factors common to all devices, principally governs the switching. For the present device structure and testing conditions, these results establish a substrate-engineering framework for dual thermoelectric functions in a single SWCNT architecture.

Indexed as

flexible thermoelectric generatorsheat flux sensorsphotothermal conversionp–n junctionssingle‐walled carbon nanotubessubstrate engineering

Identifiers

PMID42531602
PMCPMC13423488

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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.