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Chromium (Cr) exposure during gestation causes malformations in animal experiments. In this multicenter case-control study, we initially involved 130 orofacial clefts (OFCs) and 260 controls to assess the association between Cr concentration and risk for OFCs. Then, umbilical cord serum (49 vs. 119) and cord tissue (84 vs. 142) were used to validate the association between Cr and OFCs. We found that maternal serum Cr concentrations in OFC cases were significantly higher than those in controls. Compared with the lowest tertile of maternal serum Cr concentration, the highest tertile of Cr increased the risk for OFCs [OR = 2.14 (1.14-4.05)]. In the validation cohort of umbilical cord serum and tissue, higher concentrations of Cr were associated with increased risks for OFCs in a dose-dependent manner (all Ps for trends <0.05). Cr concentrations in maternal serum and cord serum showed a positive correlation. The Cr concentration in cord serum was inversely correlated with egg and milk consumption frequencies, and the Cr concentration in cord tissue was positively associated with indoor coal burning. In conclusion, prenatal Cr exposure is a risk factor for OFCs, and indoor coal burning during pregnancy may be one of the sources of Cr exposure.

This review organizes the current state of knowledge on the role of financial markets in energy transition. The originality of the study lies in the delimitation of its scope and diagnosis of research trends concerning the role of financing, innovation, and financial development sources. The study sets out to identify the role of the financial market in the energy transition process and present the state-of-the-art and main research focuses. For this purpose, a literature review was carried out based on the search results from the Web of Science database and using VOSViewer software, version 1.6.20. The analysis of 54 papers in the final sample allowed us to pinpoint the key links between financial markets and energy transition. Capital markets support green initiatives, with green bonds as a primary funding source. Blockchain and fintech technologies also significantly contribute to transition by offering innovative solutions. Additionally, a range of papers examine the costs associated with energy transition and the role of financial instruments in managing these. Regulatory challenges are another significant focus. This comprehensive analysis underscores the multifaceted relationship between financial markets and energy transition, providing insights into the current trends and the critical role of finance in fostering a sustainable future.

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.

Abstract A 500 L biomass fast pyrolysis Auger reactor was designed, constructed and experimented with biomass of Mesquite (Prosopsis juliflora) and rice straw (Oryza sativa). The thermogravimetric analysis of feed stock and the physico chemical properties of the feed and product bio-crude was done as per ASTM standard. An optimization based on Response Surface Methodology was carried out for the operating parameters chosen as: (1) reactor temperature, (2) feedstock-heat carrier ratio and (3) rotational speed of the auger reactor. The optimum bio-crude yield of 42.6 wt.% was observed at 500 °C, feedstock-heat carrier ratio 1:2 and 30 rpm for mesquite sawdust and 34.6 wt.% at 475 °C, 1:2 ratio and 50 rpm for rice straw. Among the two kinds of feedstock tested, the sawdust yielded better product under identical operating conditions. The final bio-crude have properties similar to the results that was reported in the past and has HHV- higher heating value less than petroleum fuel.

The global momentum targeting development of new energy sources has undergone rapid growth to alleviate environmental concerns, prompting demands for more sophisticated energy storage techniques. However, the true sustainability of current energy storage devices is questionable. The environmental impact of batteries extends to heavy metal-containing active materials and significant greenhouse gas emissions during processing and operation. To address these challenges, the integration of biowaste-based materials in battery components offers a pathway towards truly green and sustainable energy sources, ensuring closed carbon cycles. In this dissertation, rice hull ash (RHA) has been investigated as a biomass source providing access to novel battery components. The first part of this thesis discusses extracting SiO2 from RHA using an approach that generates distillable spirosiloxane. RHA consists of >90 wt. % amorphous SiO2 intimately mixed with unburned carbon. This method enables adjusting SiO2:C ratios simply by reacting RHA with hexylene glycol (HG) and a base catalyst to distillatively extract SiO2 to produce silica-depleted RHA (SDRHA) with SiO2 contents of 40–65 wt.%. Thereafter, the focus is on detailed studies using the SDRHA without adding extra carbon for direct carbothermal reduction. The preserved structures and coincidently eliminated alkali impurities permit the use of the resulting composites as components for electrochemical energy storage devices among other applications. The second part describes exploring multiple materials derived from SDRHA for Li+ storage. SDRHA40-60 is found to contain hard carbon (HC) structures. Electrochemical Li+ storage performance in SDRHA40-60 reveals significant contributions from the porous structures of HC in SDRHA through surface capacitive behavior and diffusion into the inner areas. Layered, cubic SiC produced from SDRHA provides another potential anode for LIBs. SDRHA-derived SiC w/wo HC (SiC/HC, SiC/O), exhibit unexpected capacity increases eventually to >950 mAh/g and >740 mAh/g, respectively, after 600 cycles. Further investigation into the lithiation mechanisms finds partial cubic to hexagonal phase transformation offers larger interstitial spaces/defects and subsequently higher capacities on cycling. Furthermore, it was found that introducing a relatively small amount of graphite to SiC/HC composites promotes rates of capacity increases while retaining sustainability. The positive effects from the coincidentally formed HC are demonstrated. Coincidentally, the feasibility of using SiNxOy as electrodes is also described. Preliminary characterization suggests elongation along the a axis in orthorhombic Si2N2O resulting in specific capacities >400 mAh/g. Introducing external Li into Si2N2O using Li2CO3 finds coincidental formation of lithium silicon oxynitride during carbonitriding. The 3D lithium conduction channels facilitate Li+ diffusion in the electrodes and consequently improve their rate performance. Experimental post-mortem studies provide insights into understanding the Li+ diffusion pathways. The last part of this thesis covers extended findings on stabilizing performance of the cobalt-free, high-voltage LiMn1.5Ni0.5O4 (LMNO) spinel cathode. By ball-milling LMNO with flame-made nanopowder (NPs, e.g., LiAlO2, LATSP, LLZO) electrolytes, the coated composites mitigate the well-recognized Mn2+ dissolution issues. Comprehensive characterization provides supportive explanation for different NPs showing various effects on the LMNO-composite cathode performance compared to pristine LMNO. The positive effects of improving Li+ diffusivities and forming protective layers are discussed in detail. Overall, the findings in this dissertation provide fundamental insights toward incorporating RHA and its derivatives in the loop to produce next-generation LIBs. The impletion of biowaste-based materials and cobalt-free cathode are two essential strategies to achieve sustainable electrification by reducing the world's dependence on mining natural sources and attaining carbon neutrality.

We begin by analyzing, using basic physics considerations, under what conditions it becomes energetically favorable to use aggressive regenerative braking to reach a lower speed over “coasting” where one relies solely on air drag to slow down. We then proceed to reformulate the question as an optimization problem to find the velocity profile that maximizes battery charge. Making a simplifying assumption on battery-charging efficiency, we express the recovered energy as an integral quantity, and we solve the associated Euler–Lagrange equation to find the optimal braking curves that maximize this quantity in the framework of variational calculus. Using Lagrange multipliers, we also explore the effect of adding a fixed-displacement constraint.

This paper proposes a novel consensus-based distributed control algorithm for solving the economic dispatch problem of distributed generators. A legacy central controller can be eliminated in order to avoid a single point of failure, relieve computational burden, maintain data privacy, and support plug-and-play functionalities. The optimal economic dispatch is achieved by allowing the iterative coordination of local agents (consumers and distributed generators). As coordination information, the local estimation of power mismatch is shared among distributed generators through communication networks and does not contain any private information, ultimately contributing to a fair electricity market. Additionally, the proposed distributed algorithm is particularly designed for easy implementation and configuration of a large number of agents in which the distributed decision making can be implemented in a simple proportional-integral (PI) or integral (I) controller. In MATLAB/Simulink simulation, the accuracy of the proposed distributed algorithm is demonstrated in a 29-node system in comparison with the centralized algorithm. Scalability and a fast convergence rate are also demonstrated in a 1400-node case study. Further, the experimental test demonstrates the practical performance of the proposed distributed algorithm using the VOLTTRON platform and a cluster of low-cost credit-card-size single-board PCs. Comment: 16 Pages, 13 figures Figures order and references are corrected!