Functional analysis of single Poly(methyl-methacrylate)-based submicron pore electrophoretic flow detectors via translocation of differently sized silica nanoparticles
Bibliographic record
Abstract
Detection and discrimination of nanoparticles is a vital step in several analytical and diagnostic procedures. Towards this, the authors present in the current study, for the first time, an all poly(methyl-methacrylate) (PMMA) polymer membrane-based solid-state sensor capable of detecting single silica nanoparticles. The sensor is based on a single cylindrical submicron pore of 450 nm in diameter and 1 [micro sign]m in length, patterned by electron beam lithography in a PMMA membrane. It was subsequently integrated into a PMMA-based electrophoretic flow detector system containing two electrolyte reservoirs. Silica nanoparticles of 100 nm in diameter were dispersed in an electrolyte and detected as they temporarily block the current flow during translocation through the submicron pore, driven by an electric field. The submicron pore was highly stable, and able to not only detect but also discriminate between silica nanoparticles of different dimensions recognised by different amounts of current blockade produced as they translocated through the pore. The translocations of individual 100 and 150 nm diameter silica nanoparticles through the single submicron pore, and thus the amounts of current blockade they produce, were shown in very close agreement with the results evaluated mathematically using the model presented in this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".