Protein crystals IR laser ablated from aqueous solution at high speed retain their diffractive properties: applications in high-speed serial crystallography
Bibliographic record
Abstract
In order to utilize the high repetition rates now available at X-ray free-electron laser sources for serial crystallography, methods must be developed to softly deliver large numbers of individual microcrystals at high repetition rates and high speeds. Picosecond infrared laser (PIRL) pulses, operating under desorption by impulsive vibrational excitation (DIVE) conditions, selectively excite the OH vibrational stretch of water to directly propel the excited volume at high speed with minimized heating effects, nucleation formation or cavitation-induced shock waves, leaving the analytes intact and undamaged. The soft nature and laser-based sampling flexibility provided by the technique make the PIRL system an interesting crystal delivery approach for serial crystallography. This paper demonstrates that protein crystals extracted directly from aqueous buffer solution via PIRL-DIVE ablation retain their diffractive properties and can be usefully exploited for structure determination at synchrotron sources. The remaining steps to implement the technology for high-speed serial femtosecond crystallography, such as single-crystal localization, high-speed sampling and synchronization, are described. This proof-of-principle experiment demonstrates the viability of a new laser-based high-speed crystal delivery system without the need for liquid-jet injectors or fixed-target mounting solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".