Two-photon absorption induced nanowelding for assembling ZnO nanowires with enhanced photoelectrical properties
Bibliographic record
Abstract
The joining of semiconductor nanowires (NWs) is fundamental for the construction and assembly of high performance nanoelectronic devices, but the development of reliable methods of nanojoining and nanowelding of these components has been elusive to date. In this work, we report a methodology for laser welding of wide bandgap NWs based on two-photon absorption. Two photon excitation during femtosecond laser irradiation leads to the generation of excitons forming an electron-hole plasma. As an application of this technique, we show that two-photon excitation is effective in the nanowelding of two ZnO NWs. A nanoweld, resulting in the formation of an interconnected structure, occurs when the energy in the solid state plasma is deposited in the contact area between the two ZnO NWs. During excitation with ultrashort laser pulses, rapid melting and solidification result in the generation and freezing out of oxygen vacancies in the irradiated area and the region near the contact between the two components. This enhances exciton trapping and energy deposition at the contact, facilitating the formation of a bond between the two NWs. It is also found that the absorption of visible light is significantly increased in ZnO NW structures assembled via two-photon femtosecond laser processing. In addition, the junction between two ZnO NWs created in this way exhibits a photoresponse that is not present prior to nanojoining. These results indicate that two-photon excitation is a promising technique for the selective deposition of thermal energy in semiconductor NWs in the absence of plasmonic interactions.
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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) | 0.001 | 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".