Robust Superhydrophobic Laser‐Induced Graphene for Desalination Applications
Bibliographic record
Abstract
Abstract The fabrication of long‐lived, durable, superhydrophobic surfaces using a manufacturable process is an important challenge for material science. Significant advances have been reported; however, many surfaces suffer from fragility, nonmanufacturable fabrication techniques, and temporal instability. Such challenges have limited commercial scale application of superhydrophobic films, including their application to water desalination where long lifetimes and durability are essential. The fabrication of controllably wettable surfaces formed from laser‐induced graphene is demonstrated in atmospheric conditions with contact angle control from 59° to 176°; representing some of the most superhydrophobic carbon surfaces ever reported. This superhydrophobicity is used to engineer a membrane with the largest pores ever reported for the energy efficient water desalination technique of air‐gapped membrane distillation. State‐of‐the‐art production of distilled water is observed, and no membrane failure or loss of superhydrophobicity is observed on a time‐scale of months—suggesting total water production capabilities well beyond anything yet demonstrated.
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) | 0.000 | 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) | 0.002 | 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".