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Optical spectroscopy and modeling of uranium gas-phase oxidation: Progress and perspectives
We report that studies related to U gas-phase oxidation through plasma- and thermo-chemistry are important for many fields, including environmental monitoring, forensic analysis, debris analysis in a weapon detonation event, and nucleation physics. Recently, significant efforts have been made to understand the chemical pathways involved in the progression from U atoms to diatoms (UO) and polyatomic molecules (UxOy), employing optical spectroscopy tools and computational modeling. In many studies, laser ablation of U or a U-containing flow reactor are used as a highly resource-efficient, repeatable, tunable, and lab-scale testbed for studying gas-phase oxidation in U plasmas. The spectroscopic analysis of high-temperature gas-phase oxidation of U is challenging due to the congested U spectra, resolution limitations of instrumentation, and the numerous chemical reaction pathways possible. This article focuses on the current understanding and challenges related to studying U plasma chemistry, specifically U gas-phase oxidation and molecular formation, via optical spectroscopy of plasmas and associated computational and spectral modeling. The physical and chemical processes involved in the evolution from U atoms to U oxide molecules to nanoparticles and agglomerates (i.e., debris) are discussed in the context of optical spectroscopic studies. The article concludes by highlighting opportunities for future research efforts based on existing knowledge published in the literature.
Measurements of the lightest hypernucleus ($_{Λ}^{3}{H}$): progress and perspective
The hyperon-nucleon (Y-N) interaction is important for the description of the equation-of-state of high baryon density matter. Hypernuclei, the cluster object of nucleons and hyperons, serve as cornerstones of a full understanding of the Y-N interaction. Recent measurements of the lightest known hypernucleus, the hypertriton’s ($_{Λ}^{3}{H}$) and anti-hypertriton’s ($\frac{H}{Λ}$$\bar{H}$) lifetime, mass and Λ separation energy have attracted interests on the subject. Its cross section and collective flow parameters have also been measured in heavy-ion collisions, which have revealed new features on its production mechanism. In this article we summarise recent measurements of $_{Λ}^{3}{H}$, focusing on the heavy-ion collisions. Here, we will discuss their implications for the $_{Λ}^{3}{H}$ properties and the constrains on the Y-N interaction models.
Responses of gaseous sulfuric acid and particulate sulfate to reduced SO 2 concentration: A perspective from long-term measurements in Beijing
SO 2 concentration decreased rapidly in recent years in China due to the implementation of strict control policies by the government. Particulate sulfate ( p S O 4 2 - ) and gaseous H 2 SO 4 (SA) are two major products of SO 2 and they play important roles in the haze formation and new particle formation (NPF), respectively. We examined the change in p S O 4 2 - and SA concentrations in response to reduced SO 2 concentration using long-term measurement data in Beijing. Simulations from the Community Multiscale Air Quality model with a 2-D Volatility Basis Set (CMAQ/2D-VBS) were used for comparison. From 2013 to 2018, SO 2 concentration in Beijing decreased by ~81% (from 9.1 ppb to 1.7 ppb). p S O 4 2 - concentration in submicrometer particles decreased by ~60% from 2012–2013 (monthly average of ~10 μg·m - 3 ) to 2018–2019 (monthly average of ~4 μg·m - 3 ). Accordingly, the fraction of p S O 4 2 - in these particles decreased from 20–30% to <10%. Increased sulfur oxidation ratio was observed both in the measurements and the CMAQ/2D-VBS simulations. Despite the reduction in SO 2 concentration, there was no obvious decrease in SA concentration based on data from several measuring periods from 2008 to 2019. This was supported by the increased SA:SO 2 ratio with reduced SO 2 concentration and condensation sink. NPF frequency in Beijing between 2004 and 2019 remains relatively constant. Finally, this constant NPF frequency is consistent with the relatively stable SA concentration in Beijing, while different from some other cities where NPF frequency was reported to decrease with decreased SO 2 concentrations.
Integration of ecosystem science into radioecology: A consensus perspective
In the Fall of 2016 a workshop was held which brought together over 50 scientists from the ecological and radiological fields to discuss feasibility and challenges of reintegrating ecosystem science into radioecology. There is a growing desire to incorporate attributes of ecosystem science into radiological risk assessment and radioecological research more generally, fueled by recent advances in quantification of emergent ecosystem attributes and the desire to accurately reflect impacts of radiological stressors upon ecosystem function. This work is a synthesis of the discussions and consensus of the workshop participant's responses to three primary questions, which were: 1) How can ecosystem science support radiological risk assessment? 2) What ecosystem level endpoints potentially could be used for radiological risk assessment? and 3) What inference strategies and associated methods would be most appropriate to assess the effects of radionuclides on ecosystem structure and function? The consensus of the participants was that ecosystem science can and should support radiological risk assessment through the incorporation of quantitative metrics that reflect ecosystem functions which are sensitive to radiological contaminants. The participants also agreed that many such endpoints exit or are thought to exit and while many are used in ecological risk assessment currently, additional data need to be collected that link the causal mechanisms of radiological exposure to these endpoints. Finally, the participants agreed that radiological risk assessments must be designed and informed by rigorous statistical frameworks capable of revealing the causal inference tying radiological exposure to the endpoints selected for measurement.
Advanced nuclear reactor integration opportunities for the pulp and paper industry in the U.S. context: Technical perspectives, gap analysis, and preliminary technoeconomic assessment
Pulp and paper (P&P) manufacturing requires a large amount of low-pressure (LP) steam to digest, wash wood fibers and dry pulp into paper. Most of the LP steam is extracted from backpressure turbines that produce power from high-pressure (HP) steam. This HP steam is generated from burning wood waste material; bark is burned in hog boilers, and lignin is boiled in a black liquor recovery boiler. In a typical integrated P&P mill, 50–100% of the steam is produced from these sources, while additional steam is produced in natural gas (NG), fuel oil, or coal boilers. The other energy-intensive process in the plant is the chemical-recovery section (e.g., lime kiln), which requires high-temperature processing from NG combustion to retrieve and recirculate spent chemicals. This paper assesses the energy and heat demand and material balances of a typical generic kraft pulp mill, along with the nuclear heat, steam, and power integration opportunities to replace conventional combustion systems. The paper also addresses steam and electricity generation through a comprehensive technical and engineering gap analysis of five different nuclear-integration opportunities and their process economics, thus enabling the lignin and bark to be further processed into biobased chemicals or fuels, as well as the potential to reduce overall emissions from kraft pulping. Preliminary findings have shown that the P&P industry could achieve technological benefits by integrating their current manufacturing process with small modular nuclear reactors (SMNRs) on a national level. This research aims to set the path forward for a cleaner and more resilient P&P industry.
Inflammation, physical activity, and chronic disease: An evolutionary perspective
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A critical perspective and analysis of two-step thermochemical fuel production cycles
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Impact of environmental variables on the degradation of photovoltaic components and perspectives for the reliability assessment methodology
Backsheet cracking has been a major issue observed in the field; however, standardized qualification tests, such as IEC61215, are inadequate to reliably identify such failures of PV modules due to the lack of the critical weathering factors applied sequentially or in combination, such as those found in the service environments. To address this problem, in this work we investigated the effects of various environmental variables on the degradation and failure behaviors of the polyamide-based backsheet in PV modules retrieved from five different locations, encompassing a variety of climates, including humid subtropical, hot-summer Mediterranean, tropical savanna climate and hot arid. The correlations between the degradation indicators and the weathering variables were further demonstrated by principle components analysis (PCA). We found strong relationships between: carbonyl formation and reflected solar radiation; hydroxyl formation and module temperature; yellowness and NO2 concentration, while no simple correlation could be found between a specific weathering factor and cracking. By introducing additional stress factors to the aged polyamide-based backsheet films with the novel 'fragmentation test', we successfully reproduced the field cracking behaviour. This study has demonstrated that different degradation modes of PV components respond differently to the environmental stresses encountered in service. Thereby, any accelerated laboratory test based on a single set condition or lacking key environmental variables would be inadequate to assess the long-term performance of PV modules and components. A new reliability-based methodology is proposed to quantitatively link laboratory testing with field results for the service life prediction of PV materials.
Perspectives on intrinsic toughening strategies and passivation of perovskite films with organic additives
We report on the inclusion of 5-aminovaleric acid (5-AVA)—a bulky, long-chained organic molecule—as an additive to enhance the mechanical integrity of hybrid perovskite films. We studied a range of organic cation additives and found that 5-AVA was the most effective at improving cohesion energy, a key metric of mechanical reliability. MAPbI 3 films reinforced with 5-AVA added in 5% concentration increased cohesion energy 12-fold from 0.53 J/m 2 to 6.04 J/m 2 , an effect which is attributed to increased plasticity and crack deflection around grain boundaries of the additive-containing perovskite. The addition of 5-AVA also improves V oc in perovskite solar cells and carrier lifetimes with a minimal decrease in PCE, attributed to passivation reducing defect and trap densities. A Tauc plot analysis of the bandgap shows that 5-AVA increases the band gap of the perovskite, correlating with reduced film stress compared to MAPbI 3 . Finally, the usage of 5-AVA improves the intrinsic thermomechanical reliability, but this improvement comes at the penalty of slightly reduced device efficiency due to reduced charge extraction from the presence of the bulky, insulating organic additive.
Conducting polymer-based electrochemical sensors: Progress, challenges, and future perspectives
Conducting polymers are promising due to their unique properties, such as excellent electrical and optical properties, physical and chemical stability, high conductivity, and effective redox properties with high-temperature stability and biocompatibility. Due to these properties, conducting polymers are useful in diverse applications like sensors, batteries, oil industries, biosensors, biomedicines, catalysis, cancer treatment, etc. This review article aims to discuss the recent trends and analysis of conducting polymer-based electrochemical sensors in diverse areas with all required sensor characteristics, such as the derived limit of detection, utilized techniques for the sensing analysis and derived linear dynamic range with the stability of the sensors. Conducting polymers and their nanocomposites-based electrochemical sensors have demonstrated exceptional capabilities towards detecting various biomolecules, heavy metals, pesticides, and viruses like SARS-COV-2. Incorporation of redox mediators, use of conducting hydrogels, and molecular imprinting are promising strategies for better performance of the derived sensor. The article has demonstrated the existing challenges and limitations and provided solutions in the field. In the future, conducting polymers-based electrochemical sensors can be utilized in wearable sensors and integrated with IoT devices for better reach in real-time applications. They can also be made more accessible with precise control and data output by following specific methodologies. Utilizing green and sustainable conducting polymers can be crucial in advancing eco-friendly practices in the future. Conducting polymer-based electrochemical sensors has affectivity in neurochemical and pathogen sensing, which is essential for brain function and mental health.
One-size-fits-all? Top-down U.S. approach to equitable decarbonization does not fully address state and community-scale perspectives
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Analysis of barriers to perceived service quality in Ghana: Students’ perspectives on bus mobility attributes
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Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling
The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.
Anhydrous volatile fatty acid extraction through omniphobic membranes by hydrophobic deep eutectic solvents: Mechanistic understanding and future perspective
Volatile fatty acids (VFAs) derived from arrested anaerobic digestion (AD) can be recovered as a valuable commodity for value-added synthesis. However, separating VFAs from digestate with complex constituents and a high-water content is an energy-prohibitive process. This study developed an innovative technology to overcome this barrier by integrating deep eutectic solvents (DESs) with an omniphobic membrane into a membrane contactor for efficient extraction of anhydrous VFAs with low energy consumption. Here, a kinetic model was developed to elucidate the mechanistic differences between this novel omniphobic membrane-enabled DES extraction and the previous hydrophobic membrane-enabled NaOH extraction. Experimental results and mechanistic modeling suggested that VFA extraction by the DES is a reversible adsorption process facilitating subsequent VFA separation via anhydrous distillation. High vapor pressure of shorter-chain VFAs and low Nernst distribution coefficients of longer-chain VFAs contributed to DES-driven extraction, which could enable continuous and in-situ recovery and conversion of VFAs from AD streams.