Rare earth oxides (REOs) are deemed important from both industrial implementation and research insight perspectives. One of the most conspicuous attributes of REOs is sensing, which contributes significantly to the development of diversified and robust systems of sensors and detector devices. However, there has not been any organized review that has pointed out critical insights from the sensor, detector, and electronic device perspectives that can invoke further studies to investigate the prospective and commercially relevant areas to date. To address this limitation, this review undertakes a focused report approach. From this concise yet comprehensive review, it has been prominent that the most significant contributions to the sensing and detecting fields by the REOs are in electrochemical, temperature, humidity, radiation, gas, and biosensors. Moreover, in terms of electronic device development, REOs have had a significant impact on memory devices, metal oxide semiconductors, dielectric materials, capacitors, energy storage devices, and so on. Furthermore, one of the key findings of the study is that the REOs have flexible doping (e.g., Er3+, Yb3+, Y3+, etc.) capability combined with other host materials such as HfO2 film, SiO2 stacks, TiO2, SnO2 nanostructures, etc., which will likely make REO-based electrochemical sensor and biosensor development the most promising sector in the coming years. Despite the impressive aspects, biocompatibility issues in several biological and biomedical systems along with the hygroscopic nature of REOs in electronic devices remain as concerns. However, these issues can be addressed by the advancement of intricate technologies such as targeted manipulation of the electronic configuration of REOs, multifarious doping experiments to obtain alternative mechanisms, etc. to obtain superior biocompatibility, and device development systems that are noninvasive to the environment. From the commercialization front, memory devices and energy storage devices will be the focusing points for large-scale investors due to improved mechanical (i.e., Young’s modulus, intrinsic stress, etc.) and electrical (i.e., high dielectric constant, resistivity, relative permittivity, etc.) properties, while REO-based metal oxide semiconductor and capacitor development is likely to be research-oriented for the next few years before making the eventual move to futuristic applications at a large industrial scale. In short, this review reports a substantial number of relevant studies that will pave the way for further experimental and computational investigations on REOs and their sensor, detector, and electronic device aspects.

In small- and large-scale industries, manipulable optical characteristics are desired. In this regard, rare-earth oxides (REOs) have been providing pragmatic attributes in terms of successful implementations and promising prospects throughout the last few decades. Currently, there is no comprehensive literature review on REOs that can aid researchers in focusing on industry-relevant emerging materials. Therefore, this review reports studies that have been able to experimentally utilize the physical, chemical, thermal, electronic, spectroscopic, and photocatalytic properties of REOs in the optical field. The brief and focused review finds that the most pronounced applications of REOs in the optical field are in white light and laser, while the prospective ground likely lies in optoelectronics, fiber optic applications, and miscellaneous repertoires that incorporate an innovative utilization of an electronic configuration of REOs. From the perspective of this review, the versatility of an REO in the optical field has become prominent and quantified by the successful implementations of REOs in white light and nonwhite light applications. Furthermore, the innovative applications of REOs include but are not limited to the development of solid-state optical devices, optoelectronic systems, and photocatalytic agents. Specifically, their futuristic applications are likely to be led by the development of stronger emission devices and the obtaining of flexible doping characteristics by several ions such as Li+, Eu3+, Dy3+, Nd3+, La3+, Yb3+, etc. at different levels, which will render the pathway for further exploration in this regard. However, the improvement in terms of methodological attributes requires a serious consideration of overcoming the limitation of thermal stability, lack of exploration of several types of lights, photodarkening in critical applications, lack of applicability at a wide range of temperatures, and so on. From an industrial perspective, it can be conjectured from the reported literature that the challenges will be overcome at a large scale within a few years due to the expedited technological advancements of the experimental repertoires, rendering the REO applications in the optical field reasonably economic and commercially viable. In short, this is the first review that objectively considers the applications and prospects of REOs, which will essentially invoke several studies to investigate the specific properties and viability of REOs in the optical field.

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, costeffective, and environmentally sustainable solutions. At the end, future advancement pathways for REO applications are also suggested.

Dissolved oxygen (DO) is a key indicator in the study of the ecological health of rivers. Modeling DO is a major challenge due to complex interactions among various process components of it. Considering the vital importance of it in water bodies, the accurate prediction of DO is a critical issue in ecosystem management. Given the intricacy of the current process-based water quality models, a data-driven model could be an effective alternative tool. In this study, a random forest machine learning technique is employed to predict the DO level by identifying its major drivers. Time-series of half-hourly water quality data, spanning from 2007 to 2019, for the South Branch Potomac River near Springfield, WV, are obtained from the United States Geological Survey database. Key drivers are identified, and models are formulated for different scenarios of input variables. The model is calibrated for each input scenario using 80% of the data. Water temperature and pH are found to be the most influential predictors of DO. However, satisfactory model performance is achieved by considering water temperature, pH, and specific conductance as input variables. The model validation is made by predicting DO concentrations for the remaining 20% of the data. The comparison with the traditional multiple linear regression method shows that the random forest model performs significantly better. The study insights are, therefore, expected to be useful to estimate stream/river DO levels at various sites with a minimum number of predictors and help build a sturdy framework for ecosystem health management across an environmental gradient.

Dissolved oxygen (DO) is a key indicator in the study of the ecological health of rivers. Modeling DO is a major challenge due to complex interactions among various process components of it. Considering the vital importance of it in water bodies, the accurate prediction of DO is a critical issue in ecosystem management. Given the intricacy of the current process-based water quality models, a data-driven model could be an effective alternative tool. In this study, a random forest machine learning technique is employed to predict the DO level by identifying its major drivers. Time-series of half-hourly water quality data, spanning from 2007 to 2019, for the South Branch Potomac River near Springfield, WV, are obtained from the United States Geological Survey database. Key drivers are identified, and models are formulated for different scenarios of input variables. The model is calibrated for each input scenario using 80% of the data. Water temperature and pH are found to be the most influential predictors of DO. However, satisfactory model performance is achieved by considering water temperature, pH, and specific conductance as input variables. The model validation is made by predicting DO concentrations for the remaining 20% of the data. The comparison with the traditional multiple linear regression method shows that the random forest model performs significantly better. The study insights are, therefore, expected to be useful to estimate stream/river DO levels at various sites with a minimum number of predictors and help build a sturdy framework for ecosystem health management across an environmental gradient.