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 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 | 0.001 |
| 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 | 0.000 | 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".