Single nickel atoms doped into TiO2 decorating carbon quantum dots for boosting photodegradation of ciprofloxacin
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Bibliographic record
Abstract
In the current work, a novel approach is developed to modulate the photocatalytic performance of titanium dioxide nanoparticles (TiO 2 NPs) via Ni doping coupled with the decoration of carbon quantum dots (CQDs). In-depth analysis using advanced techniques such as Inductively Coupled Plasma – Mass Spectroscopy (ICP-MS) and High-Resolution Transmission Electron Microscopy (HR-TEM) reveals the composition of CQDs with TiO 2 NPs and, particularly, the doping of single Nickel (Ni) atoms into the TiO 2 structure, which leads to the formation of oxygen vacancies. The synergetic effect of Ni doping and CQDs decoration results in efficient visible light absorption and charge separation. Furthermore, electrochemical techniques such as Transient Photocurrent Response (TPR) and Electrochemical Impedance Spectroscopy (EIS) were utilized to assess the electron transport efficiency under simulated light, providing insights that contributed to the proposed reaction mechanism. As a result, the prepared material exhibits enhanced degradation efficiency of ciprofloxacin (CIP), reaching up to 97 % after 150 min under irradiation by low-power household LED. The degradation reaction followed first-order kinetics, with a rate constant of k = 0.02222 min −1 , and reactive species such as hydroxyl radicals ( OH ∙ ) and superoxide radicals ( O 2 ∙ − ) played crucial roles in the reaction mechanism. This work provides a promising approach for the design of nanomaterials for efficient degradation of pollutants via photocatalysis. • Oxygen vacancy formation as a result of single Ni atom doping into TiO 2 structure • Improved light absorption and charge separation by Ni doping and CQDs decoration • Enhanced photocatalytic degradation of ciprofloxacin under household LED irradiation
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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 | 0.001 | 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 it