Tracking Molecular Signatures at ppb Sensitivity Using Fluctuational Kinetics in Metal–Organic Frameworks
Bibliographic record
Abstract
Biological systems achieve parts-per-billion (ppb) sensitivity in gas detection by tracking molecular fluctuations over time─a level of precision that remains difficult to replicate in engineered sensors. Conventional sensing relies on adsorption processes that require activation energies ( E a ) ∼10 k B T, resulting in exponentially long equilibration times and limited selectivity due to small differences in E a among analytes. Here, we show that volatile organics interacting with a ∼200 nm-thick nanoporous metal–organic framework (MOF), when subjected to shear-induced strain via a quartz crystal microbalance (QCM), exhibit a secondary fluctuational adsorption time scale distinct from the steady-state response. This emergent kinetic signature allows for reliable molecular discrimination at sensitivities down to ∼100 ppb. Our approach introduces a new selectivity metric based on dynamic adsorption kinetics, opening avenues for real-time molecular identification in environmental monitoring, portable diagnostics, and selective detection in chemically complex settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".