Bibliographic record
Abstract
Heavy metals in industrial effluents are of particular concern because of their ability to bioaccumulate in the food chain, making it possible for toxic levels to reach higher animals and plants. Chromium is widely used in processes such as leather tanning, electroplating, pigmentation and in corrosion inhibitors. Trivalent and hexavalent are the most common species used in industry, with hexavalent being widely understood to be the most toxic. It is important, therefore, to understand the mechanisms of how chromium and other metals accumulate in the environment. \nThe aim of this project was to investigate metal, and in particular chromium uptake, by live seaweeds. This was done by characterising the seaweed (ICP-OES, Kjeldahl, FTIR), using the seaweed as a biomonitor, and looking at the surface morphology of the seaweed (AFM, SEM-EDX). A set of bioaccumulation experiments under many different conditions was carried out in order to determine the effect of seaweed species (Fucus vesiculosus, Palmaria palmata, Ulva lactuca), metal species (Cr(III) vs. Cr(VI)), season (May/June versus Feb/Mar), and temperature (7 ˚C vs. 16 ˚C) on metal uptake. To the author’s knowledge, this was the first study to look at bioaccumulation by live seaweed of these two chromium species. \nCharacterisation confirmed the presence of sulphate, carboxylate, amide, and phosphorus containing groups. Seasonal and inter-species differences in seaweed composition were also identified. \nA biomonitoring study, carried out on F. vesiculosus and A. nodosum sampled in Ireland and Newfoundland, showed that both seaweeds are suitable biomonitors. Metal contents reflected the levels of pollution which were likely to be present in the sampling areas. \nSurface microscopy showed that a biofilm was present on the surface of U. lactuca. A time course study on Cr(III) binding showed that the biofilm was disrupted and reduced as metal exposure continued. Further experiments using AFM, SEM-EDX and total viable surface counts did not show differences between blank and metal loaded seaweeds. This was likely to be because the experimental conditions were slightly different, with a lower final metal concentration. To the author’s knowledge, this was the first time the removal and disruption of a seaweed biofilm was shown by AFM, and the first time metal uptake in these three species was studied using AFM. \nThe following general trends were observed for Cr accumulation: U. lactuca>P. palmata>F. vesiculosus; Cr(III) > Cr(VI); 16 ˚C > 7 ˚C; Feb/Mar > May/Jun; intracellular uptake ~ 100%. Seasonal and inter-species differences were found to be attributable to difference in composition e.g. greater levels of P or N for a particular species, or a particular season. Greater binding at higher temperatures, could be because of greater cell membrane fluidity, increased metabolism and increased protein synthesis.
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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 | 0.000 |
| 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 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".