Molecular recognition using receptor-free nanomechanical infrared spectroscopy based on a quantum cascade laser
Bibliographic record
Abstract
Speciation of complex mixtures of trace explosives presents a formidable challenge for sensors that rely on chemoselective interfaces due to the unspecific nature of weak intermolecular interactions. Nanomechanical infrared (IR) spectroscopy provides higher selectivity in molecular detection without using chemoselective interfaces by measuring the photothermal effect of adsorbed molecules on a thermally sensitive microcantilever. In addition, unlike conventional IR spectroscopy, the detection sensitivity is drastically enhanced by increasing the IR laser power, since the photothermal signal comes from the absorption of IR photons and nonradiative decay processes. By using a broadly tunable quantum cascade laser for the resonant excitation of molecules, we increased the detection sensitivity by one order of magnitude compared to the use of a conventional IR monochromator. Here, we demonstrate the successful speciation and quantification of picogram levels of ternary mixtures of similar explosives (trinitrotoluene (TNT), cyclotrimethylene trinitramine (RDX), and pentaerythritol tetranitrate (PETN)) using nanomechanical IR spectroscopy.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".