Patterning of Functional Materials by Pulsed Laser Deposition through Nanostencils
Bibliographic record
Abstract
We present how various features drawn in a miniature shadow-mask (nanostencil) can be efficiently transferred to a surface in the form of 3D nanostructures of metals (Pt, Cr), semiconductors (Ge) or complex oxides (e.g. BaTiO3) by room temperature pulsed laser deposition (PLD) and stenciling. Using the proposed method, there is no aggressive interaction with the substrate, but selective deposition of the material by simply interposing a sieve with apertures down to 100 nm between the deposition source and the substrate. Nanostenciling allows organizing the structures in given architectures, with high accuracy, while reducing drastically the number of processes present in resist-based lithography. The material deposited through the stencil mask conserves the desired functionality even at the level of the individual nanostructures. The patterning process is simple and rapid since it is not implying additional processing steps to the deposition process; it is also parallel, resist-less and without interfering with the structures natural growth dynamics. Nanostenciling can be performed in high or ultra high vacuum and is suitable for parallel prototyping of fragile or functionalized surfaces.
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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) | 0.002 | 0.001 |
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".