Electrocatalysts for environmental mitigation: Synthesis, characterization and application
Bibliographic record
Abstract
The growing environmental crisis has strengthened interest in conversion technologies within the chemical industry, particularly in the reducing of greenhouse gases and minimizing toxic chemicals. To combat these issues, research and developmental technologies have focused on sustainable and environmentally friendly practices capable of reducing harm to humans and the environment. At the heart of these technologies is electrochemistry, which uses renewable energy sources and allows tuneable, selective, and scalable reactions. Electrochemical conversion provides a sustainable route to the production of essential fuels and commodity chemicals required globally. Due to its simplicity, electrochemistry can thus be applied to many synthetic reactions. The primary goal of this research is to contribute to the ever-growing challenge observed in energy conversion and storage by synthesizing, characterizing, analysing, and evaluating electrocatalytic materials for carbon dioxide, CO2 electrocatalysis and 4-nitrophenol, 4NP electrocatalysis. These two processes are pivotal in environmental remediation and eradication of toxic chemicals. Thus, developing a path free of fossil fuel for the production of commodity chemicals is the core of this research. Electrocatalysts consisting of metal nanoparticles (Au, Pd, Cu), metal oxide (TiO2), layered double hydroxide, LDH (NiFe, CuFe) and graphene oxide, GO were synthesized and extensively characterized. Their activity was explored and compared for electrocatalytic applications (CO2 and 4NP). All electrocatalysts synthesized in this work showed significant catalytic performance: Au/rGO being the most active for CO2 electrocatalysis in non-aqueous solvents and Pd/TiO2 the most active for 4NP electrocatalysis. Ternary catalysts (Cu-TiO2/rGO) also displayed improved catalytic activity for electrochemical reduction of carbon dioxide, ERCO2 in aqueous media. The enhanced performance of catalyst materials was linked to the synergy between the pure metals (Au, Pd, and Cu) and the multifunctional support (TiO2, rGO). This research also demonstrates the qualitative development of electrochemical analytical techniques for the selective detection of CO2 reduction product. A rotating ring-disc electrode, RRDE was used as a quick analytical tool for the detection of acetic acid, AA, a product formed during ERCO2 on the ternary Cu-TiO2/rGO catalyst. Traditional characterization techniques such as nuclear magnetic resonance, NMR spectroscopy and high-performance liquid chromatography, HPLC were further employed to confirm the detection of acetic acid. Finally, the outcome of this research demonstrate that electrochemical techniques are a promising method for detection and conversion of CO2 (and other toxic chemicals) to value added products in both academic laboratories and industries. This serves as a solid foundation in improving conversion technologies.
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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".