Hydrophobic/hydrophilic patterned surfaces for directed evaporative preconcentration
Bibliographic record
Abstract
The deposition of micro- and nanolitre volumes is crucial in sessile droplet microfluidic systems. Several techniques exist for the fabrication of surfaces with patterned wettabilities; however, many of these fabrication techniques are time-consuming and complex. Here, we present a device that allows for deposition of multiple droplets within seconds followed by directed evaporative preconcentration. Hydrophobic-coated glass substrates are fashioned with hydrophilic surface energy traps (SETs) using picosecond laser micromachining. SETs can capture nanolitre volumed droplets of both aqueous and organic liquids through discontinuous dewetting. Modification of the machined hydrophilic shape yields a passive mechanism that preconcentrates analyte through evaporation. Studies and optimizations of SET parameters/dimensions (laser power, laser passes, ring/patch diameter) and their effect on patch wettability and degree of preconcentration are presented. As a demonstration, the optimized platform was used to improve the colourimetric detection of cadmium-containing aqueous samples. The optimized SET design demonstrated an 18-fold increase in colourimetric sensitivity compared to conventional milled SETs, suggesting the design would be well-suited for trace analysis. The evaporative preconcentration was also applied to MALDI-IMS analysis of peptides where it resulted in improved uniformity of deposited analyte and decreased analysis times. The rapid droplet deposition and directed evaporative approach can be tailored to provide different concentration factors and is compatible with a wide variety of detection schemes.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".