Geo-material surface modification of microchips using layer-by-layer (LbL) assembly for subsurface energy and environmental applications
Bibliographic record
Abstract
A key constraint in the application of microfluidic technology to subsurface flow and transport processes is the surface discrepancy between microchips and the actual rocks/soils. This research employs a novel layer-by-layer (LbL) assembly technology to produce rock-forming mineral coatings on microchip surfaces. The outcome of the work is a series of 'surface-mimetic micro-reservoirs (SMMR)' that represent multi-scales and multi-types of natural rocks/soils. For demonstration, the clay pores of sandstones and mudrocks are reconstructed by representatively coating montmorillonite and kaolinite in polydimethylsiloxane (PDMS) microchips in a wide range of channel sizes (width of 10-250 μm, depth of 40-100 μm) and on glass substrates. The morphological and structural properties of mineral coatings are characterized using a scanning electron microscope (SEM), optical microscope and profilometer. The coating stability is tested by dynamic flooding experiments. The surface wettability is characterized by measuring mineral oil-water contact angles. The results demonstrate the formation of nano- to micro-scale, fully-covered and stable mineral surfaces with varying wetting properties. There is an opportunity to use this work in the development of microfluidic technology-based applications for subsurface energy and environmental research.
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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 | 0.000 |
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".