PAPER PHYSICS. Paper-based device for pre-concentration of target analytes
Bibliographic record
Abstract
Abstract In this paper, we demoostrate proof-ofprinciple for a low cost paper-based Chromatographie device capable of pre-concentrating a target analyte by a factor of three thousand fold (assessed using confocal Iaser scanning microscopy (LSCM)). The device consists of a capture zone and a passive pump. The capture zone was created by immobilizing biotinylated microgel particles onto a selected area of filter paper. Due to the presence of biotin on their surface, these particles have the ability to specifically bind streptavidin, which was the target analyte selected for this study. With the incorporation of a superabsorbent passive pump, created using cross-linked poly(acrylic acid), partial sodium saltgra. fi-poly(ethylene oxide), the paper-based device was able to process a volume of dilute solution much larger than that associated with paper porosity. Flow through the device, including the passive pump, could be modeled using Darcy' s law. A simple equation was also developed to relate the concentration in the pre-concentration device to the concentration in the dilute sample. This type of device can be useful for sample pre-concentration before analysis using other methods, such as mass spectrometry.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".