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Record W2230139604 · doi:10.1038/ncomms8885

Piezotransistive transduction of femtoscale displacement for photoacoustic spectroscopy

2015· article· en· W2230139604 on OpenAlexaff
Abdul Talukdar, Mohammad Faheem Khan, Dongkyu Lee, Seonghwan Kim, Thomas Thundat, Goutam Koley

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersDivision of Electrical, Communications and Cyber SystemsNational Science Foundation
KeywordsTransduction (biophysics)Materials scienceTransducerPhotoacoustic spectroscopyOptoelectronicsDynamic rangeResponsivityNanotechnologyPhotoacoustic imaging in biomedicineOpticsChemistryPhysicsAcousticsPhotodetector

Abstract

fetched live from OpenAlex

Measurement of femtoscale displacements in the ultrasonic frequency range is attractive for advanced material characterization and sensing, yet major challenges remain in their reliable transduction using non-optical modalities, which can dramatically reduce the size and complexity of the transducer assembly. Here we demonstrate femtoscale displacement transduction using an AlGaN/GaN heterojunction field effect transistor-integrated GaN microcantilever that utilizes piezoelectric polarization-induced changes in two-dimensional electron gas to transduce displacement with very high sensitivity. The piezotransistor demonstrated an ultra-high gauge factor of 8,700 while consuming an extremely low power of 1.36 nW, and transduced external excitation with a superior noise-limited resolution of 12.43 fm Hz(-1/2) and an outstanding responsivity of 170 nV fm(-1), which is comparable to the optical transduction limits. These extraordinary characteristics, which enabled unique detection of nanogram quantity of analytes using photoacoustic spectroscopy, can be readily exploited in realizing a multitude of novel sensing paradigms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.323
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations47
Published2015
Admission routes1
Has abstractyes

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