Nanolayer Laser Absorber for Femtoliter Chemistry in Polymer Reactors
Bibliographic record
Abstract
Abstract Laser‐induced forward transfer (LIFT) has the potential to be an alternative approach to atomic force microscopy based scanning probe lithography techniques, which have limitations in high‐speed and large‐scale patterning. However, traditional donor slides limit the resolution and chemical flexibility of LIFT. Here, a hematite nanolayer absorber for donor slides to achieve high‐resolution transfers down to sub‐femtoliters is proposed. Being wettable by both aqueous and organic solvents, this new donor significantly increases the chemical scope for the LIFT process. For parallel amino acid coupling reactions, the patterning resolution can now be increased more than five times (>111 000 spots cm−2 for hematite donor vs 20 000 spots cm−2 for standard polyimide donor) with even faster scanning (2 vs 6 ms per spot). Due to the increased chemical flexibility, other types of reactions inside ultrasmall polymer reactors: copper (I) catalyzed click chemistry and laser‐driven oxidation of a tetrahydroisoquinoline derivative, suggesting the potential of LIFT for both deposition of chemicals, and laser‐driven photochemical synthesis in femtoliters within milliseconds can be explored. Since the hematite shows no damage after typical laser transfer, donors can be regenerated by heat treatment. These findings will transform the LIFT process into an automatable, precise, and highly efficient technology for high‐throughput femtoliter chemistry.
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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".