Evidence map›Paper›PMID 42303619›Full record

ArticleNature communications2026

High responsivity IR sensing based on reflectometric RF MEMS.

Melisa E Gülseren, Matthew Benson, Zhixing Lin, Tianyou Li, William P Putnam, J Sebastian Gomez-Diaz

Abstract read
In one paragraph

Article in Nature communications, 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
–field-weighted citation impact
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

6 authors.

Melisa E Gülseren *Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.ORCID http://orcid.org/0000-0002-9375-2337
Matthew Benson *Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.ORCID http://orcid.org/0009-0001-8228-534X
Zhixing Lin *Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.ORCID http://orcid.org/0000-0003-4223-7164
Tianyou LiDepartment of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.ORCID http://orcid.org/0000-0002-5878-5942
William P PutnamDepartment of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA.ORCID http://orcid.org/0000-0002-4901-461X
J Sebastian Gomez-DiazDepartment of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA. jsgomez@ucdavis.edu.ORCID http://orcid.org/0000-0002-6852-8317

Funding

Neural-network enabled nano-spectroscopy for label-free cancer detection in biofluidsR01CA273253 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Randy Carney, Juan Sebastian Gomez Diaz · 2023 to 2026
$2.8M
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) R01CA273253National Science Foundation (NSF) 2048263National Science Foundation (NSF) ECCS 2314932NCI NIH HHS R01 CA273253U.S. Department of Defense (United States Department of Defense) W911NF2210067
6 · The paper itself

Abstract

Radiofrequency microelectromechanical systems (RF MEMS) integrated with metasurfaces are promising platforms for spectrally selective infrared (IR) sensing. Conventional devices detect IR radiation by tracking resonance frequency shifts. Here, we introduce a reflectometric approach that instead monitors changes in the RF MEMS input impedance and analytically links them to sensing metrics. By coupling a reconfigurable matching network to an RF MEMS resonator, the detector achieves IR responsivities governed by its phase-slope quality factor and tunable to exceptionally high values. Using contour-mode resonators at ambient conditions, we demonstrate responsivities exceeding 11,400 V/W in a 50-Ω readout (>200 A/W), spectral selectivity with a full-width at half-maximum (FWHM) of 0.54 μm at 5.94 μm, noise-equivalent power (NEP) of

Identifiers

PMID42303619
PMCPMC13429729

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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.