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Integrated pH-Sensor for Medical Application in 180nm CMOS Technology

2019· article· en· W2965620915 on OpenAlexaff
Mounir Ouremchi, Abdelli El Boutahiri, Fouad Farah, Karim El Khadiri, Hassan Qjidaa, Ahmed Lakhassassi, Ahmed Tahiri

Bibliographic record

Venue2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversité du Québec en Outaouais
FundersCentre National de la Recherche Scientifique
KeywordsISFETCMOSField-effect transistorTransistorComputer scienceElectronic engineeringElectrical engineeringMaterials scienceOptoelectronicsEngineeringVoltage

Abstract

fetched live from OpenAlex

pH measurement has been reported as a key parameter for many applications such as biotechnology processes and clinical analysis, which this paper presents an integrated pH-sensor based on an ion-sensitive field-effect transistor ISFET to measure the blood pH, and detects if there is infection, for a value of pH different from 7.3 which conform a change in the blood acidosis. In this circuit design, we used an amplification block to increase the signal in order to release medical particles as well to send a notification to the medical team. The pH-sensor is design in TSMC 180nm CMOS technology.

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)0.001
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: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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