Effect of pH and Metal Ions on Protein–Metal Complexation Modeled by Fluorescence Quenching
Bibliographic record
Abstract
Obtaining and analyzing fluorescence spectra is a valuable skill for students taking a course in enviro-analytical chemistry. This experiment allowed students to gain an understanding of metal complexation at varying pH using a fluorescence quenching technique. Students collected fluorescence spectra of BSA quenched by a range of Cu or Pb concentrations at two pH levels and conducted a Stern–Volmer analysis. Increasing the quenching agent concentration caused a decrease in the fluorescence intensity. Students recorded and plotted the fluorescence intensities at the peak maximum ( F max ) against quenching agent concentration. The Stern–Volmer equation and modified Stern–Volmer equation were used to obtain complexation parameters: the Stern–Volmer constant of association (K sv ), the binding constant (K a ), and the stoichiometric coefficient of the metal ( n ). Copper demonstrated stronger binding with BSA at pH 5.1, while lead was a more effective quenching agent at pH 3.4. The degree of quenching decreased for both metals at pH 3.4, markedly more for Cu. This may be due to enhanced aggregation or unfolding of BSA at a lower pH, altering accessibility to binding sites. It may also be due to competitive binding with protons. Students were asked several questions related to their findings and to seek out additional research to support their ideas. Overall, students gained a molecular-level understanding of BSA–metal binding and the implications of protein conformation and denaturation at different pH levels. The questions were also used to prompt learning about environmental implications, including impacts of binding strength on the bioavailability of metal ions in the human body and aquatic systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".