Time-Resolved Mass Spectrometry for Monitoring Millisecond Time-Scale Solution-Phase Processes
Bibliographic record
Abstract
Many chemical and biochemical reactions equilibrate within a few seconds of initiation under "native" conditions. To understand the microscopic processes underlying these reactions, the most direct approach is to monitor the reaction as equilibrium is established (i.e. the reaction kinetics). However, this requires the ability to characterize the reaction mixture on the millisecond time-scale. In this review, we survey the contributions of time-resolved mass spectrometry (TR-MS) to the characterization of millisecond time-scale (bio)chemical processes, with a focus on biochemical applications. Compared to conventional time-resolved techniques, which use optical detection, the primary advantage of TR-MS is the ability to detect virtually all reactive species simultaneously. This provides a singularly high detail account of the reaction without the need for a chromophoric change on turnover or artificial chromophoric probes. To provide millisecond time-resolution, TR-MS set-ups usually employ continuous-flow rapid mixers, corresponding either to a fixed "tee" that provides a single reaction time-point or an adjustable position mixer that allows continuous reaction monitoring. TR-MS has been used to monitor processes with rates up to 500 s(-1) and to provide a detailed account of complex reactions involving 10 co- populated species. This corresponds to significantly lower time-resolution than optical methods, which can measure rates in excess of 900 s(-1) under ideal conditions, but substantially more detail (optical studies are typically limited to one or two analytes). TR-MS has been implemented on a number of platforms, including capillary and microfluidic set-ups. Capillary-based implementations are straightforward to fabricate and provide the most efficient rapid mixing. Microfluidic implementations have also been devised to enable multi-step experimental workflows that incorporate TR-MS. As a general method for time-resolved measurements, the applications for TR-MS are wide ranging. TR-MS has been used to identify intermediates in organic reactions, reveal protein (un)folding mechanisms, monitor enzyme catalysis in the pre-steady-state and, in conjunction with hydrogen-deuterium exchange, characterize protein conformational dynamics. While not without limitations, TR-MS represents a powerful alternative for measuring solution phase processes on the millisecond time-scale and a new, promising approach for revealing mechanistic details in (bio)chemical reactions.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".