Impact of pH on CdII partitioning between alginate gel and aqueous media
Bibliographic record
Abstract
Environmental context. Biogels, such as those in cell walls or biofilm matrices, generally comprise negative structural charge which leads to accumulation of positively charged species, e.g. metal ions. The magnitude of the effective charge, and hence the local chemical speciation within the gel phase, is pH dependent. In situ speciation measurements in biogels, such as the model alginate studied in this work, offer a better estimate of bioavailable concentrations than does analysis of the surrounding aqueous medium. Abstract. Many microorganisms exist in a biogel-mediated micro-environment such as a cell wall or a biofilm, in which local concentrations of ionic nutrients and pollutants differ from those in the surrounding bulk medium. The local concentration is the relevant parameter for considerations of bioavailability. These modified concentrations arise as a consequence of the negative charges within biogels which may induce a Donnan potential inside the biogel phase. For metals, the net effect on the speciation within the biogel, relative to the bulk medium, is an enhancement of the concentration of free cations. Since the structural charge in the biogel arises from protolytic functional groups, the Donnan potential is pH dependent. Here we apply in situ voltammetry to measure the free metal ion concentration inside alginate gel as a function of pH. In the pH range 3 to 7, the speciation of CdII within this model biogel can be explained by specific binding to carboxylic functional groups and electrostatic binding resulting from the Donnan potential.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".