AF4-ICPMS with the 300 Da Membrane To Resolve Metal-Bearing “Colloids” < 1 kDa: Optimization, Fractogram Deconvolution, and Advanced Quality Control
Bibliographic record
Abstract
The smallest colloids exert a disproportionately large influence on colloidal systems owing to their greater surface area; however, the challenges of working in the smaller size range have limited most field-flow fractionation-ICPMS analyses to sizes > ca. 1 kDa. We discuss considerations and present solutions for overcoming these challenges, including high pressures associated with using the 300-Da membrane, calibration in this small size range, accounting for drifting LODs and separation conditions during membrane aging, and optimizing the compromise between resolution and recovery. Necessary flow program ranges for observing pressure limits are discussed, and calibration is conducted using a combination of bromophenol blue and polystyrene size standards. The impact of membrane drift on size is demonstrated and effectively corrected by routine calibration. Separation conditions are optimized by monitoring the recovery and resolution of several trace metals. A precise, high-resolution separation is achieved using fractogram deconvolution to fully resolve overlapping peaks. Method effectiveness and precision are demonstrated through triplicate analyses of three natural water samples: M p = 2.89 ± 0.04, 3.20 ± 0.03 and 3.50 ± 0.12 kDa for DOM-associated Fe in the three samples (±95% CI). A primarily inorganic Fe fraction with M p = 14.7 ± 0.5 kDa was also resolved from the DOM-associated fraction. Quality control methods and considerations for optimizing flow conditions are detailed in the Supporting Information as a guide for researchers seeking to analyze colloids in this smallest size range using AF4-ICPMS with the 300-Da membrane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".