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At least 307 records · Page 17

A systematic multicriteria-based approach to support product portfolio selection in microalgae biorefineries

Here this work proposes and applies a sequential approach of objective methods to aid the decision-making process for the deployment of microalgae biorefineries. The strategy combines Multicriteria Decision Analysis (MCDA) and weight assignment methods to simultaneously consider technical, economic, and environmental criteria to (1) outrank the best bioproduct options from different biomass fractions present in microalgae biomass at different ratios (namely carbohydrates, lipids, and protein) and (2) define the most suitable biorefining pathways associated with specific pairings of microalgae strains and cultivation conditions. The first part of the assessment identified succinic acid, acrylic acid, and citric acid as the top-ranked bioproducts from carbohydrates, polyurethane from lipids, and thermoplastic extrusion co-feed from protein. The second step of the analysis determined that, when production of a hydrocarbon fuel is desired, the compositional profile of a strain is paramount in defining the biorefining setup that should be pursued. In summary, microalgae lipids should be sent to the production of hydrocarbon fuels if the ratio between neutral lipids and fermentable carbohydrates is higher than roughly 1, with carbohydrates and protein being converted to the higher-value products noted above. Finally, this result was corroborated through process simulations, which indicated superior economic and environmental metrics when strains are paired with suitable conversion pathways identified through MCDA based on their compositional profiles. The outcomes of this work provide clear, objective, guidelines for establishing the best biorefining approach for a large suite of biochemical compositions as a screening method prior to employing detailed process simulations alongside rigorous techno-economic and life-cycle assessments.

09 BIOMASS FUELS↗

Systematically derived thermodynamic properties for alkane oxidation

Key combustion properties, such as ignition delay time, show strong sensitivity to the thermochemistry of the main species in the standard radical oxidation pathway at low temperatures (600-1000K). Significant uncertainties persist in current estimates of thermodynamic properties, particularly for the larger species of relevance to common practical fuels. In this article, we use advanced computational schemes to evaluate thermodynamic properties for the fuel, fuel radical, peroxy, hydroperoxy-alkyl radical, and hydroperoxide species for a set of 17 fuels containing up to 9 carbon atoms, and with various degrees of branching in the alkane backbone. The procedure, termed STAR-1D, combines conformer sampling to find the minimum geometry, B2PLYP-D3/cc-pVTZ harmonic frequency evaluations, and omega B97X-D/cc-pVTZ one-dimensional torsional mapping. It includes two physically-based scaling routines: a frequency-dependent scaling to B2PLYP-D3/cc-pVTZ anharmonic frequencies and a scaling of one-dimensional omega B97X-D/ccpVTZ torsional profiles to reproduce the product of the B2PLYP-D3/cc-pVTZ frequencies in the harmonic limit. Substantive comparisons with existing experimental databases, together with careful examinations of key theoretical assumptions, are used to explore the accuracy of the predictions. These computationally intensive explorations of 195 species were facilitated by automated thermochemistry software. Further, high accuracy 0 K heats of formation from a separate study are used with the STAR-1D computations to generate NASA polynomial representations. In addition to their intrinsic value, the present results also provide a reliable database for the optimization of group additivity or machine learning schemes for scaling to larger combustion systems. Towards this end a complementary extensive conformational analysis is carried out for the medium sized species and the thermodynamic properties of the lowest energy hydrogen-bonded and non-hydrogen bonded conformers are contrasted for larger hydroperoxy-alkyl radical species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A systematic study of lanthanide titanates (A 2 Ti 2 O 7 ) chemical durability: corrosion mechanisms and control parameters

Dense lanthanide titanates ranging from La 2 Ti 2 O 7 to Yb 2 Ti 2 O 7 were synthesized by spark plasma sintering with similar micro-sized microstructure, and their chemical durability and degradation mechanisms were investigated by semi-dynamic leaching tests. Different corrosion mechanisms from incongruent to congruent dissolution were observed with preferential elemental releases of lanthanides in A 2 Ti 2 O 7 (A = La to Gd) and Ti in Er 2 Ti 2 O 7 and Yb 2 Ti 2 O 7 . Initial leaching was controlled by surface dissolution and long-term dissolution shifted to a diffusion-controlled mechanism for titanate pyrochlores from Sm 2 Ti 2 O 7 to Yb 2 Ti 2 O 7 . Both short-term and long-term leaching rates decreased from La 2 Ti 2 O 7 to Yb 2 Ti 2 O 7 , and materials became more corrosion resistant with decreasing ionic radius ratios of the rare-earth elements and titanium. Amorphous passivation films of several nm-thick enriched with Ti formed on the surfaces of the corroded materials, and the formation mechanisms were governed either by surface reorganization with preferential release of lanthanides or dissolution-precipitation from saturated bulk solutions with preferential release of titanium. Finally, these results demonstrate that the ionic size plays a dominant role in determining chemical durability of lanthanide titanates and a strong correlation is established among chemical composition/structural variation, passivation film formation and corrosion mechanisms, and materials chemical durability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A systematic exploration of boceprevir-based main protease inhibitors as SARS-CoV-2 antivirals

Boceprevir is an HCV NSP3 inhibitor that was explored as a repurposed drug for COVID-19. It inhibits the SARS-CoV-2 main protease (M Pro ) and contains an α-ketoamide warhead, a P1 β-cyclobutylalanyl moiety, a P2 dimethylcyclopropylproline, a P3 tert-butylglycine, and a P4 N-terminal tert-butylcarbamide. By introducing modifications at all four positions, we synthesized 20 boceprevir-based M Pro inhibitors including PF-07321332 and characterized their M Pro inhibition potency in test tubes (in vitro) and 293T cells (in cellulo). Crystal structures of M Pro bound with 10 inhibitors and cytotoxicity and antiviral potency of 4 inhibitors were characterized as well. Replacing the P1 site with a β-(S-2-oxopyrrolidin-3-yl)-alanyl (Opal) residue and the warhead with an aldehyde leads to high in vitro potency. The original moieties at P2, P3 and the P4 N-terminal cap positions in boceprevir are better than other tested chemical moieties for high in vitro potency. In crystal structures, all inhibitors form a covalent adduct with the M Pro active site cysteine. Here, the P1 Opal residue, P2 dimethylcyclopropylproline and P4 N-terminal tert-butylcarbamide make strong hydrophobic interactions with M Pro , explaining high in vitro potency of inhibitors that contain these moieties. A unique observation was made with an inhibitor that contains a P4 N-terminal isovaleramide. In its M Pro complex structure, the P4 N-terminal isovaleramide is tucked deep in a small pocket of M Pro that originally recognizes a P4 alanine side chain in a substrate. Although all inhibitors show high in vitro potency, they have drastically different in cellulo potency to inhibit ectopically expressed M Pro in human 293T cells. In general, inhibitors with a P4 N-terminal carbamide or amide have low in cellulo potency. This trend is reversed when the P4 N-terminal cap is changed to a carbamate. The installation of a P3 O-tert-butyl-threonine improves in cellulo potency. Three molecules that contain a P4 N-terminal carbamate were advanced to cytotoxicity tests on 293T cells and antiviral potency tests on three SARS-CoV-2 variants. They all have relatively low cytotoxicity and high antiviral potency with EC 50 values around 1 μM. A control compound with a nitrile warhead and a P4 N-terminal amide has undetectable antiviral potency. Based on all observations, we conclude that a P4 N-terminal carbamate in a boceprevir derivative is key for high antiviral potency against SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗

From models to reality: a systematic review on simulated and measured residential heat pump energy savings

High-performance HVAC solutions are central to residential energy management. A substantial share of these are electric, reversible-cycle systems, with heat pumps representing the largest portion of current and near-term adoption. This review synthesizes peer-reviewed and grey literature on residential space heating and cooling heat pumps. The academic literature is dominated by modeling (73.8%), with limited field measurement (13.1%). Grey literature from United States serve as a supplemental resource providing measured savings. Conversions from electric-resistance heating consistently show the largest site energy reductions, while oil/propane baselines yield moderate savings, and gas baseline scenario often deliver small and region-dependent savings. This study cross-checks the grey literature measured data with simulation data filtered from the ResStock dataset. The comparison indicates a discrepancy between simulations and measured data: simulated site EUIs are typically lower than measured EUIs, but percentage energy savings fall in similar ranges, implying simulations capture directional effects while underestimating energy use. Factors associated with variability and model–measurement differences include system characterization and control representation (e.g., backup heat engagement, thermostat/setpoint strategies, commissioning/installation quality), occupant behavior, weather normalization, metering scope, and envelope characterization. This paper also outlines the proposed methodology for comparing simulation and measured data for heat pumps. It emphasizes the metrics used for comparison and units harmonization, building characteristics matching, and compact metadata are needed for simulations to match measured data. The proposed methodology is expected to improve the credibility of simulated savings as measured evidence grows.

Yu, Lili↗

A systematic solution to quantify economic values of vehicle grid integration

We report Vehicle-Grid-Integration (VGI) supplies one of the potential benefit extensions for electric vehicles (EVs) to make use of their parking time, which enables the EVs to provide grid services while still meeting consumer driving needs. However, the costs, benefits and risks of VGI still remain unclear, which limits the development of the VGI to promote the interaction between the EV and grid. In this study, we propose an integrated framework to quantify and utilize the aggregate flexibility of the EVs to supply the grid services in electricity markets. The integrated solution includes five sub-modules that cover end-to-end functionalities from individual EV energy consumption estimation to final monetary values calculation of providing grid services. Both wholesale market and local level charging management are formulated in the optimization module. A predictive control algorithm is proposed to allocate power to individual vehicles in real time, considering uncertainties from dispatch signal and travel behavior. Simulation results from 10,000 EVs indicate that the proposed optimization methods can significantly reduce the system cost in both wholesale market and retail market. Local tariff optimization reduces the electricity cost by 24.4% compared to uncontrolled charging. Wholesale market optimization results show that $\$$691 and $\$$255 revenues can be captured by each EV in ERCOT and CAISO markets per year, although with a conservative assumption on battery throughput cost at 0.16$\$$/kWh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A systematic review of machine learning in groundwater monitoring

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.

AI/ML↗

A systematic literature review on residential demand response with a focus on opportunities for low-income communities

Demand Response improves efficiency and grid stability, reducing peak load and total system costs. It can potentially contribute to other societal aspects, including mitigating climate change and improving air quality, health, stress levels, and comfort. This paper reviewed the literature on residential demand response programs in the United States, through the lens of the five energy justice principles, identifying and categorizing a set of 165 papers from 1960 to 2024. As a result, we observe significant progress in distributive justice, however, mostly focused on increasing participation rather than attending specific needs of historically marginalized communities. Concordantly, recognition and procedural justice dimensions are lagging, while cosmopolitan justice studies are scarce, underscoring a need to improve the measurement of impacts resulting from the implementation of demand response programs. Future research opportunities include the consolidation of standardized definitions and metrics, to better understand burdens and needs of diverse residential users. In terms of policy, collection and transparent access to standardized data is required as a condition to the design of flexible, customizable, transparent, and easy to navigate demand response programs. As a result, a better understanding of and engagement with customers has the potential to increase adoption rates, persistence in the program, and overall impacts for the system, households, and society.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗