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Record W2019170204 · doi:10.3390/s131216657

Heater-Integrated Cantilevers for Nano-Samples Thermogravimetric Analysis

2013· article· en· W2019170204 on OpenAlexaff
Valeria Toffoli, Sergio Carrato, Dongkyu Lee, Sangmin Jeon, Marco Lazzarino

Bibliographic record

VenueSensors · 2013
Typearticle
Languageen
FieldChemistry
Topicthermodynamics and calorimetric analyses
Canadian institutionsUniversity of Alberta
FundersAssociazione Italiana per la Ricerca sul CancroMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsThermogravimetric analysisCantileverThermometerMaterials scienceResistorFinite element methodNano-Thermal analysisComposite materialThermalEngineeringStructural engineeringElectrical engineeringChemical engineeringPhysics

Abstract

fetched live from OpenAlex

The design and characteristics of a micro-system for thermogravimetric analysis (TGA) in which heater, temperature sensor and mass sensor are integrated into a single device are presented. The system consists of a suspended cantilever that incorporates a microfabricated resistor, used as both heater and thermometer. A three-dimensional finite element analysis was used to define the structure parameters. TGA sensors were fabricated by standard microlithographic techniques and tested using milli-Q water and polyurethane microcapsule. The results demonstrated that our approach provides a faster and more sensitive TGA with respect to commercial systems.

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 categoriesInsufficient payload (model declined to judge)
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 score0.532
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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