Editorial on Non-covalent capillary coatings for protein separations in capillary electrophoresis‚ÄövÑvp by C.A. Lucy, A.M. MacDonald and M.D. Gulcev
Bibliographic record
Abstract
The use of coatings applied to the surface of the capillary is a common technique used in capillary electrophoresis. Such coatings can be applied either by covalent binding to the capillary wall, or by physical (non-covalent) binding. In the latter method, the normal approaches are either to flush the capillary with the coating material prior to filling it with the background electrolyte to be used in the separation (the static coating‚ÄövÑvp or semi-permanent coating‚ÄövÑvp approach), or to include the coating material as a component of the electrolyte itself (the dynamic coating‚ÄövÑvp approach). Typically, such capillary coatings are used to modify the separation by manipulating the electroosmotic flow or in some cases, to induce selectivity changes resulting from interaction of analytes with the coating. However, when proteins are to be separated, the capillary coating serves the specific purpose of inhibiting interactions between the proteins and the capillary wall. Without this inhibition, poor peak shapes are frequently obtained. The very high propensity with which proteins, especially basic proteins, adsorb to negatively charged silanol groups on the fused-silica capillary wall has long been a major impediment to the achievement of highly efficient and reproducible separations of these species by capillary electrophoresis. Overcoming this impediment has generated intense research activity, such that there is now a very extensive range of additives available. These include a wide array of surfactants (non-ionic, cationic and zwitterionic, having single or double aliphatic chains) and both dynamic and static polymer coatings employing a very diverse range of polymers. Users of capillary electrophoresis who wish to achieve the separation of a particular set of target proteins are therefore faced with an enormous set of possible coating materials, each with specific characteristics and performance. The choice of the correct coating can therefore be a difficult task. Professor Charles Lucy and his co-workers at the University of Alberta have made major contributions to the study of capillary coating materials and I was therefore delighted that he agreed to accept my invitation to prepare a review. The review which follows is an excellent overview of the field, it provides a comprehensive evaluation of the literature, a systematic classification of capillary coating materials (including their structures, properties and effects on separation characteristics), and also includes detailed descriptions of methods for the measurement of protein adsorption. This outstanding article will be of immense value to both experts and novices in the field of protein separations using capillary electrophoresis and I am sure that it will provide an outstanding resource of information on this topic.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".