What can orthogonal surface functionalizations provide to point-of-care testing? (Conference Presentation)
Bibliographic record
Abstract
MicroAnalytical Systems (µAS) adapted to Point-of-Care Testing are expected to provide simple chemical, molecular or cellular analysis to be used directly on the field. Different formats of µAS are already classically used, from pregnancy tests to glycemia for diabetic people. Increasing µAS analytical performances involves for instance improving limits of detection, reduce time of analysis, or increase the amount of information provided per test. These improvements may be reached by using more refined technology, involving integrated technologies such as biosample processing, enzymatic reactions, fluidic circuitry and/or biosensors. However being able to fabricate and produce cheap µAS relying on miniaturized components is still a challenging goal, particularly when dealing with low concentrated species. For example, on the one hand it may be interesting to use miniaturize nanotransducers in biosensors (e.g. photonic transducer enabling both SPR and SERS thanks to nanostructuration) ; but on the other hand the transducers size reduction may prevent the targets to reach the biosensor’s active zone in a short time, because of mass transfer phenomena. Futhermore, when the sensing area is small by comparison with the other µAS zones, it targets are likely to get adsorbed on undesired surfaces. These targets are therefore lost and cannot contribute to the final, useful signal of the µAS. In these conditions the effectivity of the µAS can be questionned. Different ways are being explored to overcome such challenges, and may enable µAS for detection of low concentration targets. For instance, it is possible to perform selective chemical modifications of surfaces bearing different materials, in order to bind molecular probes only on the transducing zone, while repelling molecular targets from other material surfaces. We will show how it is possible to perform such orthogonal surfaces modifications with a submicronic spatial resolution, relying on self-assembly phenomena.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".