Application of nanosecond mid-infrared lasers in mass spectrometry imaging of intact proteins
Bibliographic record
Abstract
Picosecond mid-infrared lasers operating near 3 microns have been shown to extract fully intact biological molecules during ablation. However, the technical challenge of producing single-mode sub-ns lasers with high enough energy to drive the laser ablation process has limited their widespread use. In this study, we demonstrate a cost-effective solution, showing how a low-cost, commercially available 2.2 ns mid-infrared laser with a central wavelength of 2725 nm can drive the same extraction technique for the analysis of large intact proteins. We also show that the detection limit of this technique is comparable to the picosecond infrared laser techniques discussed previously and other state-of-the-art techniques like nano-desorption ionization mass spectrometry imaging. This work highlights the potential of using relatively low power (10 mW range) sub-10 ns lasers to enable mass spectrometry-based spatial biomolecular profiling on the micron scale, making the technology more accessible and affordable.
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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 | 0.001 | 0.000 |
| 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".