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Record W2145615867 · doi:10.1088/0960-1317/21/1/015009

Development of a combined piezoresistive pressure and temperature sensor using a chemical protective coating for Kraft pulp digester process monitoring

2010· article· en· W2145615867 on OpenAlexafffund
Abdolreza Mohammadi, Chad P. J. Bennington, Mu Chiao

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of British Columbia
FundersNational Research Council Canada
KeywordsPiezoresistive effectMaterials sciencePressure sensorKraft processComposite materialKraft paperCoatingSiliconPulp (tooth)Optoelectronics

Abstract

fetched live from OpenAlex

We have developed an integrated piezoresistive pressure and temperature sensor for multiphase chemical reactors, primarily Kraft pulp digesters (pH 13.5, temperatures up to 175 °C, reaching a local maximum of 180 °C and pressures up to 2 MPa). The absolute piezoresistive pressure sensor consisted of a large square silicon diaphragm (1000 × 1000 µm2) and high resistance piezoresistors (10 000 Ω). A 4500 Ω buried piezoresistive wire was patterned on the silicon chip to form a piezoresistive temperature sensor which was used for pressure sensor compensation and temperature measurement. A 4 µm thick Parylene HT® coating, a chemically resistant epoxy and a silicone conformal coating were deposited to passivate the pressure sensor against the caustic environment in Kraft digesters. The sensors were characterized up to 2 MPa and 180 °C in an environment chamber. A maximum thermal error of ±0.72% full-scale output (FSO), an average sensitivity of 0.116 mV (V kPa)−1 and a power consumption of 0.3 mW were measured in the pressure sensor. The sensors' resistances were measured before and after test in a Kraft pulping cycle and showed no change in their values. SEM pictures and topographical surfaces were also analyzed before and after pulp liquor exposure and showed no observable changes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.722

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 head0.008
GPT teacher head0.227
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2010
Admission routes2
Has abstractyes

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