In Situ Rapid Fabrication of Graphene–Copper Heterojunctions Using Fiber Laser Direct Writing
Bibliographic record
Abstract
Multifunctional three-dimensional heterostructures for flexible electronics have gained significant attention due to their distinctive structural formability and superior electronic and optoelectronic properties. Nevertheless, conventional fabrication techniques have yet to be optimized for flexible substrates. In this study, a straightforward fiber laser direct writing (FLDW) process is demonstrated for the simultaneous fabrication of diodes (PN junctions) and bipolar junction transistors (BJTs) on flexible polyimide substrates, which is realized through the deposition of multifunctional p- or n-type copper oxide films (CuO x ) and p- or n-type porous laser-reduced graphene oxide films (LrGO) using fiber laser ablation and deposition. The presence of both p- and n-type semiconductor films is confirmed through material characterization. The fabricated PN junctions exhibit reasonable diode rectification ratios, ranging from 20 to 220, and perform reliably under numerous operating conditions such as light–dark illumination and elevated temperatures. Furthermore, I – V curve analysis indicates that the current gain and electrical performance of printed negative–positive–negative (NPN) (or positive–negative–positive, PNP) BJTs can be tailored by adjusting the laser energy density of the FLDW process and the base gap width of the BJTs. As a proof of concept, the FLDW process is successfully employed to deposit both NPN (or PNP) BJTs composed of LrGO/CuO x heterostructures with controlled current gains. Its ease of operation, versatility, and cost-effectiveness make FLDW promising for large-scale flexible electronics fabrication.
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 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 | 0.000 |
| 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 teacher head, 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".