Direct‐Writing of Multi‐Functional Photo‐Reduced Graphene Oxide Fabric (rGOf) at the Liquid‐Air Interface with Tunable Porosity
Bibliographic record
Abstract
Abstract A multi‐functional photo‐reduced graphene oxide fabric (rGOf) created through a laser direct‐write technique at the solution surface is presented. Taking advantage of the amphiphilic nature of graphene oxide (GO) and the rapid self‐assembly of GO sheets at the liquid‐air interface, a piece of rGOf can be easily produced without any binder or supporting material in an ambient environment. Without any added processing complexity or stringent sample requirements, this accessible technique creates rGOf that exhibits tuneable microscale porosity, which enables efficient surface functionalization and active material coating. Parameters, such as the GO concentration, stabilization time, laser power, printing speed, laser focus, and post‐processing steps, can lead to vastly different rGOf morphologies. The rGOf can be made into self‐standing film or transfer printed onto any desirable substrate. Herein, it is demonstrated that the rGOf platform holds tremendous potential in Joule heating, temperature sensing, humidity sensing, tactile sensing, and ammonia sensing applications.
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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".