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Record W7133054565

Design and Simulation of Microfluidic Chip for Detection of Bacteria

2023· dissertation· W7133054565 on OpenAlexaff
Hamed Hadayeghi

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrofluidicsBacteriaMicrofluidic chipChipHost (biology)
DOInot available

Abstract

fetched live from OpenAlex

Bacterial infections and associated diseases are of high importance to be addressed in a timely yet efficient manner. In recent years, increasing antimicrobial resistance statistics around the world and a very slow rate of improvement of bacterial diseases in low-resource environments necessitates practical ways to address the issue with a reliable technique.Microfluidics merged with diffraction sensing pose a great potential for point-of-care diagnostics, specifically for capturing and concentrating the bacteria from its host such as drinking water. This study proposed an efficient technique for bacteria concentration and trapping using microfluidics. We studied the behavior of fluid and particles then adjusted corresponding parameters. While several designs were explored, we focused on periodic structures of dimensions compatible with the diffraction-based detection method invented in our group. Doubled Herringbone showed outstanding results, Reversed Herringbone and Simple Herringbone designs performed reliably (capturing bacteria with 72.3%, 54.5%, and 51.8% efficiency respectively).

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.297
Teacher spread0.272 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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