Developing Analytical Methods for Detecting Contaminants of Concern in Water Systems
Bibliographic record
Abstract
Water quality monitoring of drinking, waste, ground and surface water is crucial within our society for the benefit of the environment and human health. Various chemical contaminants of water samples may cause concern to the public for aesthetic reasons, or for the prevention of adverse health effects. As a result, Health Canada has implemented guidelines regarding concentration limits of various inorganic contaminants in water systems. Consequently, accurate, efficient and cost-effective analytical methods for analyte determination in water samples is incredibly important. Conventional water analysis techniques utilize sophisticated techniques, including colorimetry, ion selective electrodes and spectroscopic methods. Although accurate and reliable, concerns may arise as they are quite expensive. Electrochemical sensors provide solutions are they are cost-effective, portable and user-friendly methods that can be tailored towards specific target analytes. This thesis explores the use of electrochemical processes and techniques for the application of chemical sensing in water samples. More specifically, the design and optimization of two electrochemical sensors with immobilized self-assembled monolayers for application in chemical sensing were performed. The first sensor utilized a pyridine-zinc(II) complex for the determination of phosphate ions, at an ultrasensitive scale. Within these studies, optimization of the monolayer and analysis on phosphate binding with the zinc metal center was performed. Analysis of tap and lake water samples was demonstrated with this sensor as well. The second sensor utilized a simple pyridine-terminated monolayer for Cd(II) determination in water. However, water coadsorption interferences were also observed. As a result, optimization of the sensor signal was completed with the aim to enhance the sensor’s performance. Electrochemical techniques such as cyclic voltammetry and square-wave voltammetry, and surface characterization techniques such as x-ray photoelectron spectroscopy, were employed in these investigations.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".