Precise Photochemical Post‐Processing of Molecular Crystals
Bibliographic record
Abstract
Molecular crystals carry a great potential as new soft smart materials, with a plethora of recent examples overcoming the major obstacle of mechanical flexibility, and this research direction holds enormous potential to revolutionize optics, electronics, medicine, and space exploration. However, shaping organic crystals into desired shapes and sizes remains a major practical challenge due to the lack of control over the crystallization process, and the difficulties in mechanical post-processing without introduction of defects that are usually imparted by their soft nature. Here we present an innovative approach that employs photochemical processing for precise and nondestructive cutting of a molecular crystal. Our proposed method uses light to post-process crystals of the desired size and shape, similar to using light to cut other materials. This reaction induces strain, ensuring sharp cleavage without the need for melting or other processes. We further demonstrate the potential of this approach by producing crystals of arbitrary size, which can be used as controllable optical waveguides. Among other potential applications, this method can be used to prepare dynamic crystals, particularly those with aspect ratios crucial for mechanical deformation, such as flexible electronics, soft robotics, and sensing.
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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 | 0.001 |
| 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".