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Record W2941928455 · doi:10.1088/1361-6528/ab1b93

Self-powered, flexible and remote-controlled breath monitor based on TiO <sub>2</sub> nanowire networks

2019· article· en· W2941928455 on OpenAlexaff
Yu Xiao, Daozhi Shen, Guisheng Zou, Aiping Wu, Lei Liu, W. W. Duley, Y. Zhou

Bibliographic record

VenueNanotechnology · 2019
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceNanowireWearable computerHumidityVoltageWearable technologyElectronicsSensitivity (control systems)Transmission (telecommunications)NanotechnologyComputer scienceElectrical engineeringElectronic engineeringEmbedded systemTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Abstract Smart breath monitor devices with high stretchability, fast response/recovery times and self-powered characteristic are essential in the wearable medical and life science applications. In this work, we report on the development of a versatile high-performance humidity sensor based on TiO 2 nanowire networks for self-powered sensing application of human breath monitoring. These sensors, with typical response times of ∼3.6 s and recovery times of ∼14 s, exhibit high sensitivity to water vapor and can yield an output voltage that is directly proportional to the humidity level of ambient environment. The structure of nanowire networks is highly flexible and maintains the output voltage even after 10 000 times bending. By combining this type of sensor with a commercial signal transmission and processing system, it shows the good basis for real-time/remote-controlled monitoring and analysis of human breath under a variety of respiratory conditions. Our results suggest a new class of humidity sensing for self-powered biomedical devices and open to new technologies in energy, electronics, and sensor applications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.003
GPT teacher head0.175
Teacher spread0.172 · 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.

Study designBench or experimental
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

Citations30
Published2019
Admission routes1
Has abstractyes

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