Continuous Langmuir–Blodgett Deposition and Transfer by Controlled Edge-to-Edge Assembly of Floating 2D Materials
Bibliographic record
Abstract
The Langmuir-Blodgett technique is one of the most controlled methods to deposit monomolecular layers of floating or surface active materials but has lacked the ability to coat truly large-area substrates. In this work, by manipulating single-layer dispersions of graphene oxide (GO) and thermally exfoliated GO into water-immiscible spreading solvents, unlike traditional Langmuir-Blodgett deposition which requires densification achieved by compressing barriers, we demonstrate the ability to control the 2D aggregation and densification behavior of these floating materials using a barrier-free method. This is done by controlling the edge-to-edge interactions through modified subphase conditions and by utilizing the distance-dependent spreading pressure of the deposition solvent. These phenomena allow substrates to be coated by continuous deposition and substrate withdrawal-enabling roll-to-roll deposition and patterning of large-area substrates such as flexible polyethylene terephthalate. The aggregation and solvent-driven densification phenomena are examined by in situ Brewster angle video microscopy and by measuring the local spreading pressure induced by the spreading solvent acting on the floating materials using a Langmuir-Adam balance. As an example, the performance of films deposited in this way is assessed as passivation layers for Ag nanowire-based transparent conductors.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".