Encapsulating Laser‐Induced Graphene to Preserve its Electrical Properties and Enhance its Mechanical Robustness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it