Application of Sorel cement containing magnesium carbonate as concrete fine aggregate for sequestering CO2: material and structural performance
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Lim, Sopokhem (ORCID:0000000282629958)↗
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Soft Nanoconfinement Nucleates and Stabilizes Ultrasmall Amorphous Calcium Carbonate from Aggregation
Organisms use soft confinement structures, such as vesicles and compartments, to direct the nucleation of calcium carbonate (CaCO 3 ) and its subsequent processes during biomineralization. Despite recent efforts elucidating confinement’s effects on CaCO 3 polymorph selection, we still poorly understand how the size and distribution of CaCO 3 are controlled within soft confinement. Here, using a size-controlled nanoemulsions system made from isooctane, Span 80, Tween 80, and aqueous solutions, we studied CaCO 3 formation in soft confinement. Small angle X-ray scattering (SAXS) confirmed that a 72 nm aqueous core in nanoemulsions served as the confined space for CaCO 3 formation. Unlike the ~ 50 nm CaCO 3 particles that formed in the unconfined solution, small angle neutron scattering (SANS) and transmission electron microscope (TEM) showed that ultrasmall and amorphous calcium carbonate precipitated within soft confinement and did not exhibit any aggregation/coalescence of nanoparticles even after 24 hrs of reaction.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Formation of Unusual Deprotomers of Citric Acid and Sodium Citrate Aggregates: A Photoelectron Spectroscopy and Computational Study
Given multiple distinct deprotonation sites and flexible structural conformation in citric acid (CA), the intrinsic interaction between protons, sodium cations and citrate anions deems complicated. In this work, we address this issue by combining photoelectron spectroscopy and theoretical modeling on a series of gaseous sodium citrate anion clusters with progressive substitution of protons with sodium cations [(CA-nH)·(n-1)Na]- (n = 1-3). Despite markedly different pKa values for the central (C, 3.28), terminal carboxylic (T, 5.98) and hydroxyl group (O, 18.44), our study reveals experimental observation of all three distinct deprotomers in [CA-1H]- with deprotonation from hydroxyl energetically competitive. Upon Na+ binding to the doubly or triply deprotonated CA, only one kind of deprotonation tautomer, respectively TC or TTC, is experimentally accessible. Moreover, a dianionic dimer [(CA-3H)·2Na]22- is observed. This work displays a diverse binding landscape among protons, sodium and citrate species in the gas phase, advancing our understanding of sodium citrate chemistry.
Yuan, Qinqin↗
Aggregation Dynamics of Colloidal Particles in Tin Perovskite Crystalline Film Formation
We present an approach to understanding the crystallization of tin-based perovskite films for photovoltaic applications, starting from precursor suspensions processed via spin-coating. By integrating colloidal theory with the fluid dynamics of suspensions, this approach elucidates the influence of both chemical variables and process parameters on the crystallization pathways of perovskite suspensions and the resulting microstructural features of the solid films. Specifically, the incorporation of SnCl 2 as an additive was found to accelerate crystallization, whereas tBP induces a slowdown of the process leading, however, to a marked improvement in film uniformity and microstructural quality.
Crystallization↗
Optically accessible long-lived electronic biexcitons at room temperature in strongly coupled H- aggregates
Not provided.
Science & Technology - Other Topics↗
Insights into water extraction and aggregation mechanisms of malonamide-alkane mixtures
Structure at the nanoscale in the organic phase of liquid–liquid extraction systems is often tied to separation performance. However, the weak interactions that drive extractant assembly lead to poorly defined structures that are challenging to identify. Here, in this work, we investigate the mechanism of water extraction for a malonamide extractant commonly applied to f-element separations. We measure extractant concentration fluctuations in the organic phase with small angle X-ray scattering (SAXS) before and after contact with water at fine increments of extractant concentration, finding no qualitative changes upon water uptake that might suggest significant nanoscopic reorganization of the solution. The critical composition for maximum fluctuation intensity is consistent with small water–extractant adducts. The extractant concentration dependence of water extraction is consistent with a power law close to unity in the low concentration regime, suggesting the formation of 1 : 1 water–extractant adducts as the primary extraction mechanism at low concentration. At higher extractant concentrations, the power law slope increases slightly, which we find is consistent with activity effects modeled using Flory–Huggins theory without introduction of additional extractant–water species. Molecular dynamics simulations are consistent with these findings. The decrease in interfacial tension with increasing extractant concentration shows a narrow plateau region, but it is not correlated with any change in fluctuation or water extraction trends, further suggesting no supramolecular organization such as reverse micellization. This study suggests that water extraction in this system is particularly simple: it relies on a single mechanism at all extractant concentrations, and only slightly enhances the concentration fluctuations characteristic of the dry binary extractant/diluent mixture.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantifying electron transport in aggregated colloidal suspensions in the strong flow regime
Electron transport in complex fluids, biology, and soft matter is a valuable characteristic in processes ranging from redox reactions to electrochemical energy storage. These processes often employ conductor–insulator composites in which electron transport properties are fundamentally linked to the microstructure and dynamics of the conductive phase. While microstructure and dynamics are well recognized as key determinants of the electrical properties, a unified description of their effect has yet to be determined, especially under flowing conditions. In this work, the conductivity and shear viscosity are measured for conductive colloidal suspensions to build a unified description by exploiting both recent quantification of the effect of flow-induced dynamics on electron transport and well-established relationships between electrical properties, microstructure, and flow. These model suspensions consist of conductive carbon black (CB) particles dispersed in fluids of varying viscosities and dielectric constants. In a stable, well-characterized shear rate regime where all suspensions undergo self-similar agglomerate breakup, competing relationships between conductivity and shear rate were observed. To account for the role of variable agglomerate size, equivalent microstructural states were identified using a dimensionless fluid Mason number, Mn f , which allowed for isolation of the role of dynamics on the flow-induced electron transport rate. At equivalent microstructural states, shear-enhanced particle–particle collisions are found to dominate the electron transport rate. This work rationalizes seemingly contradictory experimental observations in literature concerning the shear-dependent electrical properties of CB suspensions and can be extended to other flowing composite systems.
Science & Technology - Other Topics↗
Aggregated DER_A Model Parameterization via Online Moving Horizon Estimation
Here, this paper introduces a methodology for parameterizing the DER_A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER_A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodology’s effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER_A model closely mirrors the real system’s voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration.
42 ENGINEERING↗
Aggregated Distributed Energy Resources in 2024: The Fundamentals
This report builds upon existing literature and leading examples of ADER pricing and programs in practice to equip commissioners and staff at Public Utilities Commissions (PUCs) and State Energy Offices with the fundamentals of ADER grid services, valuation options, and approaches to compensation. Examples, active debates, and opportunities for further reading are highlighted throughout the document for readers to explore. This report does not seek to outline a specific objective for ADERs in the electricity system nor does it recommend any specific policies for ADERs—it seeks to provide helpful information to inform policymakers’ own ADER strategies and priorities.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation
The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.
15 GEOTHERMAL ENERGY↗
From zonal to nodal capacity expansion planning: Spatial aggregation impacts on a realistic test-case
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Engineering - Power transmission and distribution↗