Encapsulating Laser‐Induced Graphene to Preserve its Electrical Properties and Enhance its Mechanical Robustness
Bibliographic record
Abstract
Laser‐induced graphene (LIG) has gained significant attention as a promising material for various applications, including flexible electronics, due to its high electrical conductivity, ease of fabrication, and cost‐effective production. However, its fragile structure makes it susceptible to degradation under mechanical stress and harsh environments. Existing encapsulation techniques compromise LIG's conductivity, limiting its practical applications. Herein, an encapsulation method that enhances the mechanical durability while preserving its electrical properties is introduced. The LIG exhibits an initial sheet resistance of 2.2 Ω sq −1 , which is among the lowest values ever achieved. Using a pressure of 80 psi, LIG is encapsulated with a polyimide layer, resulting in a minimal resistance increase of only 5%. Comprehensive characterization, including Raman spectroscopy and scanning electron microscopy, confirms that the encapsulation approach maintains the structural integrity of LIG while significantly improving its resilience to bending and environmental factors such as moisture and temperature fluctuations. Additionally, initial cyclic loading tests demonstrate the encapsulated LIG's ability to retain most of its conductive properties after the first mechanical deformation. These findings highlight the potential of this encapsulation technique for advancing flexible and wearable electronic devices, paving the way for more durable, high‐conductivity graphene‐based technologies.
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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) | 0.000 | 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".