Automating multistep computational chemistry tasks remains challenging because reasoning, workflow specification, software execution, and high-performance computing (HPC) execution are often tightly coupled. We demonstrate a decoupled agent-skill design for computational chemistry automation leveraging OpenClaw. Specifically, OpenClaw provides centralized control and supervision; schema-defined planning skills translate scientific goals into executable task specifications; domain skills encapsulate specific computational chemistry procedures; and DPDispatcher manages job execution across heterogeneous HPC environments. In a molecular dynamics (MD) case study of methane oxidation, the system completed cross-tool execution, bounded recovery from runtime failures, and reaction network extraction, illustrating a scalable and maintainable approach to multistep computational chemistry automation.
Background:
The postpartum period is a vulnerable phase associated with significant physiological and psychological changes. Post-partum blues and parenting stress are common among post-caesarean section mothers and may adversely affect maternal wellbeing and postnatal outcomes. Non-pharmacological nursing interventions such as Emotional Freedom Technique (EFT) and web-based nursing interventions have emerged as feasible approaches to address these concerns.
Aim:
To assess and compare the effectiveness of Emotional Freedom Technique and web-based nursing intervention on post-partum blues, parenting stress and postnatal outcomes among post-caesarean section mothers.
Methods:
A pilot, pre-experimental, two-group pretest–posttest design was conducted among 20 post-caesarean section mothers selected using purposive sampling. Participants were allocated to Experimental Arm I (EFT, n = 10) and Experimental Arm II (web-based nursing intervention, n = 10). Post-partum blues, parenting stress and postnatal outcomes were assessed using the Likert “Am I Blue?” Assessment Scale, Parenting Stress Scale and selected biological parameters. Interventions were administered for six weeks, three sessions per week, each lasting 30 min. Descriptive and inferential statistics were used for analysis.
Results:
Experimental Arm I showed a significant reduction in post-partum blues from severe levels (90%) to mild levels (70%), with marked improvement in parenting stress and postnatal outcomes. The highest mean percentage change was observed in postnatal outcomes (76%). Experimental Arm II also demonstrated significant improvement, with maximum change in postnatal outcomes (62%). Unpaired t-test analysis indicated that EFT was significantly more effective than the web-based intervention (P < 0.05).
Conclusion:
Both interventions were effective; however, Emotional Freedom Technique demonstrated superior outcomes, supporting its integration into postnatal nursing care.
Ananthu Shibu Nair, Xiao-Yu Wu, Prodip K. Das
et al.
Lithium-ion batteries (LIBs) are pivotal in electric vehicles (EVs), grid storage, and portable electronics, but their high energy density introduces safety risks, particularly thermal runaway (TR). TR can lead to fires, explosions, and hazardous emissions, posing severe health and environmental threats. Experimental investigation of TR commonly relies on abuse testing methods, among which mechanical abuse via nail penetration (NP) and thermal abuse (TA) are widely used to simulate crash-induced and heat-driven failure scenarios, respectively. This review provides a comprehensive and comparative synthesis of NP and TA testing methodologies, examining how variations in test configuration, cell parameters (capacity, state of charge, and chemistry), and environmental conditions influence TR behavior and emission characteristics. Particular emphasis is placed on comparing reported emission profiles from NP- and TA-triggered TR events, including CO<sub>2</sub>, CO, HF, hydrocarbons, and solvent vapors, and identifying the methodological origins of discrepancies across studies. By systematically linking emission variability to gas collection methods, analytical techniques, and data normalization approaches, this review highlights key limitations in current testing standards related to emission characterization. Finally, recommendations are offered for harmonizing abuse testing protocols and improving experimental design to enhance reproducibility, enabling meaningful cross-study comparison, and supporting safer deployment of LIBs in high-risk applications such as EVs and grid-scale energy storage.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Large Language Model (LLM)-based agents have demonstrated the ability to improve performance in chemistry-related tasks by selecting appropriate tools. However, their effectiveness remains limited by the inherent prediction errors of chemistry tools. In this paper, we take a step further by exploring how LLMbased agents can, in turn, be leveraged to reduce prediction errors of the tools. To this end, we propose ChemHAS (Chemical Hierarchical Agent Stacking), a simple yet effective method that enhances chemistry tools through optimizing agent-stacking structures from limited data. ChemHAS achieves state-of-the-art performance across four fundamental chemistry tasks, demonstrating that our method can effectively compensate for prediction errors of the tools. Furthermore, we identify and characterize four distinct agent-stacking behaviors, potentially improving interpretability and revealing new possibilities for AI agent applications in scientific research. Our code and dataset are publicly available at https: //anonymous.4open.science/r/ChemHAS-01E4/README.md.
Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.
Jonas Weidner, Christian N. Tchassem, Debanjan Das
et al.
Abstract Electrochemical CO2 conversion is an important strategy to produce high‐value carbon‐containing molecules, such as ethylene and ethanol. Despite huge progress in recent years concerning CO2 reduction catalyst development with increased selectivity, high selectivity for C2+ products at high current densities is still a challenge. We report the development and optimization of a new surface Al‐rich Cu/CuOx catalyst with high selectivity for C2+‐products at high current densities of up to −800 mA cm−2. We integrated the corresponding catalyst‐modified gas‐diffusion electrode into a second flow‐through electrolyzer, which was connected to a first flow‐through electrolyzer comprising a highly CO‐selective Ni−Cu dual‐atom N‐doped carbon catalyst. The enrichment of the CO2 stream with CO generated at a current density of −400 mA cm−2 in the first electrolyzer increased the production rate of ethanol formation at the Al‐rich Cu/CuOx catalyst at a current density of −300 mA cm−2 by 28 %, while maintaining the production rate of ethylene. Thereby, the overall yield of C2+‐products obtained by CO2 reduction was significantly increased.
Aim:
The study focused to determine the microleakage of various materials using the dye penetration method.
Materials and Methods:
The study samples consisted of 45 healthy human mandibular premolar teeth removed for orthodontic therapy without caries. The samples were categorized into three groups of 15 each. All the samples were etched for 20 seconds with 37% phosphoric acid gel before being sealed with a glass ionomer cement (GIC) sealant (Fuji—VII GIC), light-cure glass ionomer composite cement (Prevest Fusion I), or flowable nanocomposite sealants (Prevest Fusion Flo), respectively. Every sample were thermocycled and immersed for one day in methylene blue (5%) solution to dye diffusion between the gaps present among the tooth and restoration. The teeth were sectioned and analyzed using image analysis software beneath a stereomicroscope at 10x magnification.
Results:
The marginal microleakage was found lowest in flowable nanocomposites (1.06 ± 0.98), then light-cure glass ionomer composite cement (2.44 ± 1.42), and GIC (4.07 ± 1.54). A statistically significant variance was noted.
Conclusion:
Current investigation evaluated that reduced marginal leakage was seen in flowable nanocomposites when compared to other two groups.
Achal D. Warhade, Akash More, Namrata Anjankar
et al.
This case study reviews the use of Lymphocyte Immunotherapy (LIT) in a 28-year-old female patient who had a history of recurrent first-trimester miscarriages and failed intracytoplasmic sperm injection (ICSI) cycles. She had good ovarian reserve, normal semen analysis, and negative autoimmune tests but experienced four early miscarriages, which suggested an immune-mediated issue. LIT was chosen as a treatment option to address the possibility of immune system dysregulation contributing to pregnancy loss. LIT is the process of infusing the husband’s lymphocytes to promote maternal tolerance of paternal antigens, reducing the possibility of immune foetal rejection. A received LIT was performed six weeks prior to a fresh ICSI cycle, with successful fertilization and transfer of embryos. There is evidence that positive outcomes occurred in serial ultrasounds and hormone levels at 6, 8, and 12 weeks with subsequent birth at term of a healthy baby. This case points to the possibility of using LIT as a therapeutic intervention in the treatment of unexplained recurrent miscarriage in patients who had previously failed previous assisted reproductive treatments. While the mechanism of action for LIT remains unknown, it would seem to somehow modulate the immune responses so that pregnancy results in better outcomes. This case may thus provide new evidence for LIT as a possible management strategy for those with immune causes of recurrent pregnancy loss (RPL) but warrants additional studies to optimize its use and effectivity.
Background:
Surgical site infections (SSIs) are a serious concern in cesarean sections, leading to research on improved preventive measures, to evaluate the effectiveness of standard antibiotic prophylaxis alone versus an extended regimen that includes azithromycin in reducing SSIs in nonelective cesarean sections.
Methods:
In this randomized controlled trial, 288 women who were undergoing nonelective cesarean sections at a tertiary care hospital in South India were involved. Participants were divided into two groups: one receiving standard prophylaxis with cefazolin and the other receiving an extended regimen with azithromycin. The main focus was on the occurrence of SSIs within six weeks after the surgery.
Results:
The extended regimen group had a slightly lower incidence of SSIs (1.42%) compared to the standard regimen group (6.12%), but this difference did not reach statistical significance (P = 0.112).
Conclusion:
Although the inclusion of azithromycin alongside standard prophylactic antibiotics demonstrated a slight decrease in SSIs, the results did not reach statistical significance. These findings indicate potential advantages in certain patient groups, which should be explored in more detail.
The problems of laser-induced fluorescence (LIF) measurements in a partially saturated regime with spatially dependent laser intensity in the sample (caused by absorption) are analyzed. The obtained equations are tested by means of LIF of free tellurium atoms in a plasma of an atmospheric pressure dielectric barrier discharge (DBD) by comparing fluorescence and absorption measurements. The results show a high reliability of LIF measurements.
Rakesh K. Jha, Meghali Kaple, Ranjit S. Ambad
et al.
Background:
Sickle cell disease (SCD), thalassemia, and glucose-6-phosphate dehydrogenase (G6PD) deficiency are significant genetic disorders prevalent in Central India, particularly among tribal populations. Early detection through the neonatal screening can improve health outcomes.
Aim and Objective:
This study aims to assess the prevalence of SCD, thalassemia, and G6PD deficiency in a cohort of newborns from tribal regions in Central India and to evaluate the effectiveness of neonatal screening programs.
Materials and Methods:
A total of 382 newborns were screened using high-performance liquid chromatography (HPLC) for hemoglobinopathies and a colorimetric method for G6PD deficiency. Data on demographics and family history were collected and analyzed.
Results:
The screening revealed 22 cases of SCD (5.8%), 37 cases of thalassemia (9.7%), and 29 cases of G6PD deficiency (7.6%). A significant correlation was found between family history and the prevalence of these disorders.
Conclusion:
The findings highlight the need for comprehensive neonatal screening programs in tribal populations to enhance early detection and management of genetic disorders.
Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves. Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. Moreover, these models lack access to external knowledge sources, limiting their usefulness in scientific applications. In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. By integrating 18 expert-designed tools, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and Chemcrow's performance. Our work not only aids expert chemists and lowers barriers for non-experts, but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.
Emil Dimitrov, Goar Sanchez-Sanz, James Nelson
et al.
Accurate and scalable methods for computational quantum chemistry can accelerate research and development in many fields, ranging from drug discovery to advanced material design. Solving the electronic Schrodinger equation is the core problem of computational chemistry. However, the combinatorial complexity of this problem makes it intractable to find exact solutions, except for very small systems. The idea of quantum computing originated from this computational challenge in simulating quantum-mechanics. We propose an end-to-end quantum chemistry pipeline based on the variational quantum eigensolver (VQE) algorithm and integrated with both HPC-based simulators and a trapped-ion quantum computer. Our platform orchestrates hundreds of simulation jobs on compute resources to efficiently complete a set of ab initio chemistry experiments with a wide range of parameterization. Per- and poly-fluoroalkyl substances (PFAS) are a large family of human-made chemicals that pose a major environmental and health issue globally. Our simulations includes breaking a Carbon-Fluorine bond in trifluoroacetic acid (TFA), a common PFAS chemical. This is a common pathway towards destruction and removal of PFAS. Molecules are modeled on both a quantum simulator and a trapped-ion quantum computer, specifically IonQ Aria. Using basic error mitigation techniques, the 11-qubit TFA model (56 entangling gates) on IonQ Aria yields near-quantitative results with milli-Hartree accuracy. Our novel results show the current state and future projections for quantum computing in solving the electronic structure problem, push the boundaries for the VQE algorithm and quantum computers, and facilitates development of quantum chemistry workflows.
The analysis of the mid-infrared spectra helps understanding the composition of the gas in the inner, dense and warm terrestrial planet forming region of disks around young stars. ALMA has detected hydrocarbons in the outer regions of the planet forming disk and Spitzer detected \ce{C2H2} in the inner regions. JWST- MIRI provides high spectral resolution observations of \ce{C2H2} and a suite of more complex hydrocarbons are now reported. Interpreting the fluxes observed in the spectra is challenging and radiation thermo-chemical codes are needed to properly take into account the disk structure, radiative transfer, chemistry and thermal balance. Various disk physical parameters like the gas-to-dust ratio, dust evolution including radial drift, dust growth and settling can affect the fluxes observed in the mid-IR. Still, thermo-chemical disk models were not always successful in matching all observed molecular emission bands simultaneously. The goal of this project is two-fold. We analyse the warm carbon chemistry in the inner regions of the disk, i.e. within 10 au to find pathways forming \ce{C2H2} potentially missing from the existing chemical networks. Second, we analyse the effect of the new chemistry on the line fluxes of acetylene. We use radiative thermo-chemical disk code {P{\small RO}D{\small I}M{\small O}} to expand the hydrocarbon chemistry that occurs in a typical standard T Tauri disks. We used the UMIST and the KIDA rate databases for collecting reactions for the species. We include a number of three-body and thermal decomposition reactions from STAND2020 network. We included isotopomers for the species that were present in the databases. The chemistry is then analysed in the regions that produce observable features in the mid-infrared spectra. The effect of expanding the hydrocarbon chemistry on the mid-infrared spectra is studied. Acetylene is formed via two ....
Abstract Diyarbakır City Walls, one of the longest defensive structures in the world, following the Great Wall of China, the walls of Antakya, and the walls of Istanbul, is a UNESCO World Heritage site since 2015. With a history of approximately 5000 years, the Diyarbakır City Walls have been affected by consecutive earthquakes centered in Kahramanmaraş in 2023, resulting in damages to various sections. Urgent restoration and repair interventions are needed for these sections of the Diyarbakır City Walls due to earthquake-induced damages. Although there are limited studies presenting stone analysis of the Diyarbakır City Walls in the literature, no studies focusing on mortar analysis have been found. The objectives of this study are as follows: (I) to identify the mechanisms and factors of earthquake damages in the Diyarbakır City Walls, (II) to conduct necessary analyses for the selection of mortar materials for post-earthquake repairs, and (III) to provide restoration and strengthening recommendations to ensure the sustainability of the original structure. Observational, petrographic, chemical, and SEM analysis techniques were used, and the findings were interpreted comparatively. The results demonstrate that the most severe damages after the earthquake in the Diyarbakır City Walls were caused by the inadequate adhesion of missing mortar joints and different types of materials used between double-walled structures. Additionally, the presence of clay minerals identified in the mineralogy of the mortar through experimental analysis was defined as an internal issue causing the loss of mortar due to osmotic pressure created by water absorption. Another factor causing the loss of mortar is the presence of chloride-type salts, which were found to be present in a significant amount in all samples and were attributed to the use of Portland cement in previous faulty repairs. It was also determined that recent faulty repointing works contributed to the loss of mortar. Finally, this article presents original restoration and strengthening recommendations to repair the earthquake-induced damages and prevent their reoccurrence in the future.
Esther Gómez-Mejía, Iván Sacristán, Noelia Rosales-Conrado
et al.
Obtaining polyphenols from horticultural waste is an emerging trend that enables the valorization of resources and the recovery of value-added compounds. However, a pivotal point in the exploitation of these natural extracts is the assessment of their chemical stability. Hence, this study evaluates the effect of temperature storage (20 and −20 °C) and drying methods on the phenolic composition and antioxidant activity of clementine and lemon peel extracts, applying HPLC-DAD-MS, spectrophotometric methods, and chemometric tools. Vacuum-drying treatment at 60 °C proved to be rather suitable for retaining the highest antioxidant activity and the hesperidin, ferulic, and coumaric contents in clementine peel extracts. Lemon extracts showed an increase in phenolic acids after oven-drying at 40 °C, while hesperidin and rutin were sustained better at 60 °C. Hydroethanolic extracts stored for 90 days preserved antioxidant activity and showed an increase in the total phenolic and flavonoid contents in lemon peels, unlike in clementine peels. Additionally, more than 50% of the initial concentration was maintained up to 51 days, highlighting a half-life time of 71 days for hesperidin in lemon peels. Temperature was not significant in the preservation of the polyphenols evaluated, except for in rutin and gallic acid, thus, the extracts could be kept at 20 °C.
Patrick Reiser, Marlen Neubert, André Eberhard
et al.
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
Olha Mykhailenko, Liudas Ivanauskas, Ivan Bezruk
et al.
The application of the Quality by Design (QbD) concept to extracts obtained from <i>Crocus sativus</i> perianth with potential anticancer activity will ensure the safety, efficiency, and quality control of the entire technological process, as well as determine the critical factors affecting the quality of extracts. Potentially critical points of the production of the plant extracts, including the cultivation and processing of the plant materials, the extraction process, and the choice of solvents, were identified using the Ishikawa diagram and FMEA risk assessment methods as well as the corrective actions proposed. The Herbal Chemical Marker Ranking System (HerbMars) approach was used to justify the Q-markers choice of <i>Crocus</i>, which takes into account bioavailability, pharmacological activity, and the presence of the selected standard. An experimental design (DoE) was used to assess the influence of potentially critical factors on the efficiency of the compound extraction from raw materials with water or ethanol. The presence of 16 compounds in <i>Crocus</i> perianth was determined by HPLC and their quantitative assessment was established. Selected compounds (ferulic acid, mangiferin, crocin, rutin, isoquercitrin) can be used for the quality control of <i>Crocus</i> perianth. In addition, the stigmas from the Volyn region met the requirements of ISO 3632 for saffron as a spice (category I). The cytotoxic activity against melanoma (IGR39) and triple-negative breast cancer (MDA-MB-231) cell lines of the hydroethanolic extract of <i>C. sativus</i> perianth was significantly more pronounced than the water extract, probably due to the chemical composition of the constituent components. The results show that the QbD approach is a powerful tool for process development for the production of quality herbal drugs.