Selective breaking and re-joining of CuO nanowires by nanosecond laser irradiation
Bibliographic record
Abstract
Nanostructures incorporating copper oxide (CuO), a narrow bandgap p-type semiconductor, are well suited for applications such as gas/biosensors, field emission devices, and photodetectors. However, the use of CuO nanocomponents in these applications is currently limited by the availability of fabrication and in situ processing techniques. In this paper, we show that the electrical and mechanical properties of CuO nanowire (NW) networks can be adjusted through sequential processing with nanosecond laser radiation. This new two-stage process involves selective breakage/cleaving of CuO NWs with an initial set of laser pulses, followed by irradiation with a second set of laser pulses applied in an optimized orientation to tailor bonding and junction formation between pairs and bundles of previously separated CuO NWs. We find that stage one processing introduces a high concentration of oxygen vacancies in NWs leading to the nucleation of dislocations and high strain. This localized strain is responsible for the breaking of individual NWs, while the high oxygen vacancy concentration modifies the electrical conductivity within each NW. The second stage involves re-orientation of the laser beam, followed by additional laser irradiation of the NW network. This has been found to result in the bonding of NWs and the creation of junctions in regions where CuO NWs are in contact. Laser-induced heating under these conditions produces melting in the contact areas between NWs and is accompanied by the reduction of CuO to form Cu2O as verified via XPS and Raman analysis. XRD and TEM observations demonstrate that plastic deformation within CuO NWs dominates in stage one laser processing. The enhancement of electrical conductivity observed, following stage two processing, is attributed due to an increase in the concentration of laser-induced oxygen vacancies as well as the formation of localized bridging and junction sites in the overall NW network.
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".