Measuring the Time-evolution of Nanoscale Materials with Stopped-flow and Small-angle Neutron Scattering
Bibliographic record
Abstract
This paper presents the use of a stopped-flow small-angle neutron-scattering (SANS) sample environment to quickly mix liquid samples and study nanoscale kinetic processes on time scales of seconds to minutes. The stopped-flow sample environment uses commercially available syringe pumps to mix the desired volumes of liquid samples that are then injected through a dynamic mixer into a quartz glass cell in approximately 1 s. Time-resolved SANS data acquisition is synced with the sample mixing to follow the evolution of the nanostructure in solution after mixing. To make the most efficient use of neutron beam time, we use a series of flow selector valves to automatically load, rinse, and dry the cell between measurements, allowing for repeated data collection throughout multiple sample injections. Sample injections are repeated until sufficient neutron scattering statistics are collected. The mixing setup can be programmed to systematically vary conditions to measure the kinetics at different mixing ratios, sample concentrations, additive concentrations, and temperatures. The minimum sample volume required per injection is approximately 150 µL depending on the path length of the quartz cell. Representative results using this stopped-flow sample environment are presented for rapid lipid exchange kinetics in the presence of an additive, cyclodextrin. The vesicles exchange outer-leaflet (exterior) lipids on the order of seconds and fully exchange both interior and exterior lipids within hours. Measuring lipid exchange kinetics requires in situ mixing to capture the faster (seconds) and slower (minutes) processes and extract the kinetic rate constants. The same sample environment can also be used to probe molecular exchange in other types of liquid samples such as lipid nanoparticles, proteins, surfactants, polymers, emulsions, or inorganic nanoparticles. Measuring the nanoscale structural transformations and kinetics of exchanging or reacting systems will provide new insights into processes that evolve at the nanoscale.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".