Probing the Interfacial Charge-Transfer Process of Uniform ALD Semiconductor–Molecule–Metal Models: A SERS Study
Bibliographic record
Abstract
Among all coating methods, atomic layer deposition (ALD), which can provide a precise thickness control at the angstrom or the monolayer level, appears to be one of the most promising techniques. To investigate the interfacial charge-transfer mechanism from semiconductor–molecule–metal systems, the order of different layers is very essential because the charge-transfer process can be affected by the interfacial contact order of different materials. Also, for TiO 2 /MBA/Ag charge-transfer (CT) investigation, homogeneous assembling of TiO 2 with precisely controllable thickness is of great importance because the energy level of semiconductor is sensitive to its size at the nanoscale. Here, unlike previous 3D composite CT models, our semiconductor–molecule–metal interfacial CT models are fabricated with the ALD and e-beam evaporation techniques, which ensures the accuracy of the CT investigation. The surface-enhanced Raman scattering (SERS) technique is adopted in the investigation of the interfacial charge-transfer process through the changes of CT-sensitive bands. In TiO 2 /MBA/Ag, the SERS signal of MBA molecules and the Raman spectra of TiO 2 phonon vibrational mode exhibit evident CT-driven changes. To confirm these phenomena, 4 nm thickness of wide-bandgap HfO 2 and Al 2 O 3 are inserted as isolated layers. Also, the possible CT mechanisms and the charge-transfer degree in different systems are discussed. This work not only suggests a role for ALD in fabricating CT models but also promotes the application of SERS in more intensive fields.
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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".