Current Applications and Future Potential of Rare Earth Oxides in Sustainable Nuclear, Radiation, and Energy Devices: A Review
Bibliographic record
Abstract
To date, rare earth oxides (REOs) have proven to be key components in generating sustainable energy solutions, ensuring environmental safety and economic progress due to their diverse attributes. REOs’ exceptional optical, thermodynamic, and chemical properties have made them indispensable in a variety of sophisticated technologies, including electric vehicle magnets, portable energy devices, fuel cell catalysts, radiation shielding, dosimetry, and many others. Therefore, the successful incorporation of rare earth elements (REEs) into host materials in controlled concentrations offers competitive advantages to fabricate portable energy devices, radiation sensors, and radiation shielding glasses, as well as to improve the performance of existing photovoltaic cells, which is of great interest to both researchers and industry. As the global demand for REEs grows rapidly, it is critical to comprehend the underlying physics as well as the wider consequences of REEs on sustainable energy and nuclear technologies, both in the near and long term. However, despite their relevance, a focused review on the applications, prospects, and challenges of REOs in photovoltaics, nuclear, and energy devices is still unavailable. To this effort, this review succinctly reports recent experimental studies on eight REOs (R 2 O 3, R = Yb, Er, Sm, Eu, Y, Gd, Dy, and Ce) and their specific applications and industrial aspects. While several subdomains are reported, the applications of REOs in next-generation solar cells and photovoltaic devices for promoting zero-emission clean energy and rechargeable batteries for electric vehicles (EVs) are the most pioneering ones. Furthermore, REOs’ chemical stability and compositional versatility allow them to be used in a variety of high-efficiency energy converters, including solid oxide fuel cells (SOFCs). From the perspective of thermodynamic and structural stability, the gamma and neutron absorptivity of REO-doped (such as Dy 3+, Eu 3+, Sm 3+, Nd 3+, etc.) glasses shows improved shielding performance in radiation domains. Aside from the applications, the prospects of REOs presented in this article are likely to encourage current and future scholars to pursue a wide range of important studies in the fields of energy and nuclear systems. This review also reports the key challenges (i.e., material degradation, phase transformation, magnetic entropy shift, etc.) associated with REOs in a standalone section. These challenges demand the immediate attention of scientists and engineers for efficient, cost-effective, and environmentally sustainable solutions. At the end, future advancement pathways for REO applications are also suggested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".