Sample Injector Fabrication and Delivery Method Development for Serial Crystallography using Synchrotrons and X-ray Free Electron Lasers
Bibliographic record
Abstract
abstract: Sample delivery is an essential component in biological imaging using serial diffraction from X-ray Free Electron Lasers (XFEL) and synchrotrons. Recent developments have made possible the near-atomic resolution structure determination of several important proteins, including one G protein-coupled receptor (GPCR) drug target, whose structure could not easily have been determined otherwise (Appendix A). In this thesis I describe new sample delivery developments that are paramount to advancing this field beyond what has been accomplished to date. Soft Lithography was used to implement sample conservation in the Gas Dynamic Virtual Nozzle (GDVN). A PDMS/glass composite microfluidic injector was created and given the capability of millisecond fluidic switching of a GDVN liquid jet within the divergent section of a 2D Laval-like GDVN nozzle, providing a means of collecting sample between the pulses of current XFELs. An oil/water droplet immersion jet was prototyped that suspends small sample droplets within an oil jet such that the sample droplet frequency may match the XFEL pulse repetition rate. A similar device was designed to use gas bubbles for synchronized “on/off” jet behavior and for active micromixing. 3D printing based on 2-Photon Polymerization (2PP) was used to directly fabricate reproducible GDVN injectors at high resolution, introducing the possibility of systematic nozzle research and highly complex GDVN injectors. Viscous sample delivery using the “LCP injector” was improved with a method for dealing with poorly extruding sample mediums when using full beam transmission from the Linac Coherent Light Source (LCLS), and a new viscous crystal-carrying medium was characterized for use in both vacuum and atmospheric environments: high molecular weight Polyethylene Glycol.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".