An injection filling method for packing chromatography devices
Bibliographic record
Abstract
• Simple method for packing chromatography device. • Resin slurry is injected directly into a pre-assembled chromatography device. • Effect of indentation on inner surface of device is examined. • Suitable for technique demonstrated by packing anion exchange resin. • Suitability of technique demonstrated by packing size exclusion chromatography resin. An injection filling method for packing resin media in a chromatography device for protein separation is discussed in this paper. The method is first demonstrated by packing anion exchange resin within a cuboid chromatography device and a squat column, both having 7.5 mL bed volume. The method is further demonstrated by packing size exclusion chromatography media in a 50 mL cuboid chromatography device. Overall, the packing method is simple and is less demanding in terms of requirement for operator skill and experience. The devices packed using the injection filling method had excellent separation efficiency attributes. Flow through and eluted protein peaks obtained using a device with an intentional minor indentation on the inner surface of the chromatography device showed pre-peaks (or fronting) and these peaks were wider than those obtained with a device without such an indentation. Surface imperfections had a greater impact on eluted peaks than on flow-through peaks. Size exclusion chromatography experiment carried out at high flow rates showed that protein separation obtained with the 50 mL cuboid device packed using the injection filling method was superior to that obtained with a conventional 50 mL column packed with the same media. At a high flow rate. the resin-bed within the column compacted very significantly while no such compaction was observed in the cuboid device.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".