Synthesis and Characterization of Dy<sub>2</sub>O<sub>3</sub>@TiO<sub>2</sub> Nanocomposites for Enhanced Photocatalytic and Electrocatalytic Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Industrial wastewater pollution is a crucial global issue due to the increasing need for clean water. Traditional photocatalytic methods for eliminating harmful dyes are often ineffective and are environmentally damaging. This study introduces a new, efficient photocatalyst combining Dy 2 O 3 with TiO 2 using a single-step hydrothermal approach. Dy 2 O 3 @TiO 2 nanostructures were synthesized and characterized by using XRD, SEM, EDS, TEM, BET, and UV–visible spectroscopy. Dy 2 O 3 was evenly distributed on TiO 2, preventing clumping and resulting in a larger surface area with more active sites. UV irradiation (365 nm) replaced the traditional thermal energy for photocatalytic dye breakdown, leveraging the varying conductivity of the Dy 2 O 3 @TiO 2 nanocomposites. Incorporating Dy 2 O 3 decreased band gaps, enhancing redox reactions and expanding the range of degradable contaminants. For Rhodamine B dye degradation, the Dy 2 O 3 @TiO 2 composite demonstrated significantly higher degradation rates than Dy 2 O 3 or TiO 2 alone at reaction parameters such as neutral pH (pH 7) and catalyst concentration (2 g L –1 ). The hybrid material also demonstrated improved electrocatalytic activity in oxygen reduction reactions (ORRs) under alkaline conditions with an initial potential of 0.88 V and a Tafel slope of 73 mV dec –1 . The enhanced catalytic activity and durability are attributed to the synergistic interaction between Dy 2 O 3 and TiO 2 . This novel photocatalyst offers a sustainable alternative for treating industrial effluents while reducing the environmental impact.
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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) | 0.001 | 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".