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Record W4393973618 · doi:10.1117/12.3012595

Laser micromachining for enhancing lateral flow assay colorimetric signal sensitivity

2024· article· en· W4393973618 on OpenAlexaff
Gazy Albedry, Mohamed Ahmed Baba, Martynas Simanavičius, Laimis Silimavičius, Gintautas Gylys, Tomas Tamulevičius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsSensitivity (control systems)Surface micromachiningSIGNAL (programming language)LaserMaterials scienceFlow (mathematics)OptoelectronicsComputer scienceOpticsElectronic engineeringEngineeringPhysicsFabricationMedicine

Abstract

fetched live from OpenAlex

Although lateral flow assays (LFAs) are currently being a handful of diagnostic technologies that can identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other common respiratory viruses in one strip, it remains a grand challenge to substantially enhance their sensitivity. We propose here a straightforward method to overcome such drawbacks by employing nitrocellulose (NC) membrane femtosecond laser micromachining to control the analyte flow rate. The findings provided in this work indicate that tailoring the diameters of the μ-channels in NC can effectively expedite the immunological reaction time between the analyte and the labeled antibody, leading to an observable signal increase compared to pristine LFA.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.353

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 head0.216
Teacher spread0.208 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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