Direct laser synthesis, tuning, and patterning of metal nanoparticles-decorated graphene for flexible temperature sensors
Bibliographic record
Abstract
Driving the enhancement of intelligence in everyday life requires low-cost sensors to translate the physical world into data and help developing the Internet of Things (IoT) ecosystem. Direct laser writing of low-cost graphene-based sensors with commercial lasers is a promising strategy for customized fabrication of sensing platforms. This study presents an all-laser-based fabrication technique for highly sensitive, durable and conformable temperature sensing devices made of engineered organic-inorganic nanostructures. We propose rapid fabrication of graphene–metal heterojunctions as a key solution to tune the temperature sensitivity of graphene by modifying the Seebeck coefficient. By integrating different metal nanoparticles (MNPs) into the graphene matrix including nickel, cobalt, and copper, the electrothermal properties of the composites could be tuned for various sensing applications. Incorporation of copper nanoparticles into laser-induced graphene (Cu-NPs@LIG) significantly enhanced the temperature sensitivity, achieving a sensitivity of up to −1.04 %/°C for ambient and −3.44 %/°C for sub-zero temperature ranges with high linearity (R 2 > 0.98) and minimal hysteresis. Building on the initial findings, the study further investigates the interesting effects of polymer coatings on temperature sensing performance. It was observed that applying coatings such as polyimide (PI) and polyvinylidene fluoride (PVDF) on the Cu-NPs@LIG sensors significantly improved the sensitivity of the sensors up to 81 %. The environmental stability of the Cu-NPs@LIG sensors was evaluated in a closed chamber under varying humidity levels, where PVDF-coated sensors exhibited excellent stability with consistent sensitivity and minimal baseline drift. The proposed fabrication process provides a rapid, low-cost, and scalable route for high-performance flexible temperature sensors, unlocking new opportunities for applications in healthcare monitoring, smart packaging, soft robotics, and IoT-based systems.
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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".