Laser-Induced Graphene for Advanced Sensing: Comprehensive Review of Applications
Bibliographic record
Abstract
Laser-induced graphene (LIG) and Laser-scribed graphene (LSG) are both advanced materials with significant potential in various applications, particularly in the field of sustainable sensors. The practical uses of LIG (LSG), which include gas detection, biological process monitoring, strain assessment, and environmental variable tracking, are thoroughly examined in this review paper. Its tunable characteristics distinguish LIG (LSG), which is developed from accurate laser beam modulation on polymeric substrates, and they are essential in advancing sensing technologies in many applications. The recent advances in LIG (LSG) applications include energy storage, biosensing, and electronics by steadily advancing efficiency and versatility. The remarkable flexibility of LIG (LSG) and its transformative potential in regard to sensor manufacturing and utilization are highlighted in this manuscript. Moreover, it thoroughly examines the various fabrication methods used in LIG (LSG) production, highlighting precision and adaptability. This review navigates the difficulties that are encountered in regard to implementing LIG sensors and looks ahead to future developments that will propel the industry forward. This paper provides a comprehensive summary of the latest research in LIG (LSG) and elucidates this innovative material's advanced and sustainable elements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".