Additive manufacturing of architected Ca(OH)2 monoliths for accelerated CO2 mineralization

Carbon Capture Science & Technology · 2026-01-07

• Printable Ca(OH) 2 ink was developed for DIW 3D printing of structured sorbents • Achieved >99% carbonation across wide CO 2 concentrations from 10 mol% to 400 ppm • The printed monolith exhibits a low pressure drop structure suitable for scalable CO 2 capture • Techno-economic analysis (TEA) demonstrates the feasibility of DAC via DIW 3D printing Effective CCU technologies demand robust, scalable sorbents with high CO 2 selectivity and capacity. While calcium hydroxide (Ca(OH) 2 ) offers high CO 2 uptake via mineral carbonation, its practical application is hindered by slow reaction kinetics and difficulties in forming mechanically stable, structured beds. Here, we report a direct ink writing (DIW) approach to fabricate 3D-printed Ca(OH) 2 monoliths using water-based inks formulated with carboxymethyl cellulose (CMC). Under humid conditions (RH95, 1 − 10 mol% CO 2 ), the monolith achieves > 99% conversion to calcium carbonate, with similarly strong performance maintained at 400 ppm CO 2 relevant to direct air capture (DAC). The structured sorbent also exhibits extremely low pressure drop (∼2 Pa/cm), making it suitable for scaled-up applications. A techno-economic analysis (TEA) for the DAC case, incorporating parallel nozzle printing, shows that the levelized cost of capture (LCOC) can be reduced to 339 US$/tCO 2 , with break-even scenarios attainable through carbon subsidies or high-value reuse of the monolith reuse. Overall, this work establishes a dry, scalable pathway for fabricating reactive structured Ca(OH) 2 sorbents for CCU applications.

Dynamic optimization of coke formation in dry reforming of methane using a neural quasi-steady-state model

Chemical Engineering Journal · 2026-03-25

Diagnosing rapidly degrading lithium ion battery cells using direct current internal resistance

Chemical Engineering Journal · 2025-06-10 · 3 citations

Abstract 2489: The new approach for measuring nonuniformity of read coverages reveals the quality of RNA-seq data

Cancer Research · 2025-04-21

Abstract High-quality RNA is crucial for obtaining reliable RNA sequencing (RNA-seq) data, with metrics like RNA Integrity Number (RIN). However, these metrics, while effective for evaluating RNA integrity, do not always correlate with RNA-seq data quality, especially at the transcript level. This gap is particularly evident in total RNA-seq, where existing measures such as the coefficient of variation (CV) for read coverage fail to fully capture data quality and are influenced by confounding factors like read coverage depth. To address this, we developed a novel method to assess RNA-seq data quality by quantifying nonuniformity in read coverage while minimizing the influence of read coverage depth. In this study we invented new matric called windowCV (wCV) and applied to a diverse range of RNA-seq datasets, including fresh frozen (FF) and FFPE total RNA-seq data, as well as poly(A)-enriched mRNA-seq data. In mRNA-seq, our method captured 3' read coverage bias, a hallmark of RNA degradation, particularly in longer transcripts. For total RNA-seq data, we identified noisy coverage patterns associated with poor data quality, even in samples with high RIN values. By fitting regression lines between wCV and mean coverage depth (MCD) and calculating the area under the curve (wCVAUC), we refined the assessment to account for RNA quality variability. Using the TCGA pilot study and our own datasets, we demonstrated that wCVAUC reliably identified low quality RNA-seq data and highlighted its impact on downstream analyses, including gene expression quantification and clustering. Importantly, we observed that low RIN values do not always predict poor RNA-seq data quality, as some samples with RIN values below 7 exhibited high-quality RNA-seq data based on wCVAUC. It means that our analyses showed that wCVAUC effectively distinguished high-quality from low-quality samples, including cases where traditional metrics like RIN and CV were insufficient. Additionally, our investigation into the relationships between nonuniformity of read coverage, exon GC content, and RNA localization revealed that the transcript-level RNA-seq data quality of lncRNA genes in FFPE samples is influenced by low exon GC content and nuclear localization. In conclusion, our method provides robust, transcript-level metrics for assessing RNA-seq data quality across platforms, enabling more accurate identification of low-quality data and minimizing biases in downstream analyses. This approach offers a new standard for integrating RNA-seq data quality with sample variability, particularly for challenging datasets such as FFPE and total RNA-seq. Citation Format: Wonyoung Choi, Miyeon Yeon, Jay Lee, Hyo Young Choi, David Neil Hayes. The new approach for measuring nonuniformity of read coverages reveals the quality of RNA-seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2489.

Health-conscious charging of lithium-ion batteries using Bayesian optimization guided by a semi-empirical aging model

Journal of Energy Storage · 2025-11-19

Strategies for achieving a techno-economic threshold of 1 USD/kg for green hydrogen

Chemical Engineering Journal · 2025-08-11 · 3 citations

Design and analysis of wind-based hydrogen production using rule-based operation

Renewable and Sustainable Energy Reviews · 2025-02-04 · 6 citations

Tuning Pore Chemistry of Metal–Organic Frameworks Using Anionic Pillars for Humid-Tolerant CO ₂ Capture

Chemistry of Materials · 2025-10-31 · 4 citations

Reducing global CO2 emissions is a critical challenge, and metal–organic frameworks (MOFs) have emerged as promising physisorbents for capturing trace amounts of CO2 from wet flue gas and humid ambient air. The tunability of the MOF pore chemistry through functional moieties enables selective CO2 capture over H2O. In this study, 18 hypothetical MOFs (hMOFs) were rationally designed by integrating chemical moieties previously explored for trace CO2 capture. Anionic pillars (SiF62– and SO42–), known to induce strong interactions with electrophilic CO2, were incorporated into template MOFs (CALF20, CALF20-met-w, and CALF20-met-e) that have demonstrated efficacy in post-combustion CO2 capture. These anionic pillars create nucleophilic pore environments that enhance the selectivity of CO2 under humid conditions. Among the candidates, CALF20-SiF6-met-w, composed of Zn metal, methyl-triazolate, and SiF62– anionic pillars, theoretically maintained CO2 uptake efficiency above 92.4% across the entire relative humidity range, outperforming its template MOF and benchmark materials. To evaluate its practical applicability, we integrated this material into a temperature–vacuum swing adsorption (TVSA) process simulation. Parametric analysis revealed that it offers a more favorable trade-off between productivity and energy consumption than the template MOF, primarily attributable to its higher working capacity and lower H2O uptake under varying humidity conditions. This study demonstrates the potential of anion pillar engineering in MOFs to achieve efficient CO2 capture under humid conditions.

Health-Conscious Charging of Lithium-Ion Batteries Using Bayesian Optimization Guided by a Semi-Empirical Aging Model

SSRN Electronic Journal · 2025-01-01

Integrated Scheduling of Alkaline Water Electrolysis with Solar–Battery Systems, Degradation Modeling, and Thermal Management

SSRN Electronic Journal · 2025-01-01