Hasil untuk "Analytical chemistry"

P. L. Searle

Xuewan Wu, Rui Huang, Yanting Liu et al.

Exploring sensing mode with new detection signal is of great significance in analytical chemistry. In this study, we introduce a new sensing mode that employs the deformation of liquid crystal (LC) droplets as a new detection signal. As a proof-of-concept, we fabricated SDS-coated liquid crystal (LC) droplets, where the degree of deformation shows a positive correlation with the concentration of the target analyte, lead ions (Pb2+). The limit of detection (LOD) for Pb2+ was determined to be 106.4 μg/mL, which is lower than the other metal ions. It is worth noting that preliminary experimental results demonstrate that other surfactants (such as sodium alpha-olefin sulfonate, AOS) and organic phase (such as vegetable oil) also could be employed to fabricate the droplets and show different response to metal ions, which show high flexibility of the as-proposed detection method.

M. A. Fernandez, J. Boiteux, M. Espino et al.

The concept of sustainable development has impacted in analytical chemistry changing the way of thinking processes and methods. It is important for analytical chemists to consider how sample preparation can integrate the basic concepts of Green Chemistry. In this sense, the replacement of traditional organic solvents is of utmost importance. Natural Deep Eutectic Solvents (NADES) have come to light as a green alternative. In the last few years, a growing number of contributions have applied these natural solvents proving their efficiency in terms of extraction ability, analyte stabilization capacity and detection compatibility. However, the arising question that has to be answered is: the use of NADES is enough to green an extraction process? This review presents an overview of knowledge regarding sustainability of NADES-based extraction procedures, focused on reported literature within the timeframe spanning from 2011 up to date. The contributions were analyzed from a green perspective in terms of energy, time, sample and solvent consumption. Moreover, we include a critical analysis to clarify whether the use of NADES as extraction media is enough for greening an analytical methodology; strategies to make them even greener are also presented. Finally, recent trends and future perspectives on how NADES-based extraction approaches in combination with computational methodologies can contribute are discussed.

Aditya S. Dupare, Mukta Motwani, Aarati Panchbhai et al.

Temporomandibular joint disorders (TMD) involve pain and dysfunction of the temporomandibular joint and associated muscles. Emerging evidence suggests associations with bruxism, poor sleep quality, anxiety disorders, and tension-type headache (TTH). To systematically review the associations between bruxism, sleep quality, anxiety disorders, and TTH in patients with TMD. Systematic searches were conducted in PubMed, Scopus, Web of Science, and Cochrane Library up to May 2025. Studies included adults with TMD and reported associations with bruxism, sleep quality, anxiety, or TTH. Risk of bias was assessed using the Newcastle-Ottawa Scale. Data were narratively synthesized. Twenty-six studies were included. Bruxism prevalence ranged from 45% to 87% in TMD patients, with significant associations with myofascial pain (P < 0.05). Poor sleep quality was reported in 40–75% of TMD patients and correlated with pain severity. Anxiety disorders were present in 30–60% of TMD patients, often exacerbating pain perception. TTH coexisted in 25–65% of TMD patients, sharing central sensitization as a mechanism. Study heterogeneity prevented meta-analysis. Bruxism, poor sleep quality, anxiety disorders, and TTH frequently coexist with TMD. Multidisciplinary assessment is crucial. Future longitudinal studies are warranted.

Pooja Arora, Shinu Singla, B. Radhika et al.

Background: Periodontitis and cardiovascular disease (CVD) are both chronic inflammatory conditions with growing evidence of a potential link. Objective: This study aimed to assess the relationship between periodontitis and cardiovascular disease by evaluating clinical parameters and systemic inflammatory markers. Methods: A cross-sectional observational study was conducted involving 90 patients aged 35–70 years, divided into three groups: Group A (periodontitis with CVD), Group B (periodontitis only), and Group C (CVD only). Periodontal parameters such as probing pocket depth (PPD), clinical attachment loss (CAL), plaque index (PI), and bleeding on probing (BOP) were recorded. Results: Group A showed the most severe periodontal disease (PPD: 5.2 ± 1.1 mm; CAL: 4.8 ± 0.9 mm) and the highest inflammatory marker levels (CRP: 5.8 mg/L; IL-6: 12.5 pg/mL). Significant correlations were found between CAL and CRP (r = 0.62, P = 0.001), PPD and systolic BP (r = 0.47, P = 0.003), and BOP and IL-6 (r = 0.51, P = 0.002). Group A also had poorer oral hygiene practices and a higher prevalence of CAD (80%). Conclusion: It is concluded that periodontitis is significantly associated with cardiovascular disease, primarily through systemic inflammation.

Kefeng Huang, Jonathon Pipe, Alice E. Martin et al.

Chemistry laboratory automation aims to increase throughput, reproducibility, and safety, yet many existing systems still depend on frequent human intervention. Advances in robotics have reduced this dependency, but without a structured representation of the required skills, autonomy remains limited to bespoke, task-specific solutions with little capacity to transfer beyond their initial design. Current experiment abstractions typically describe protocol-level steps without specifying the robotic actions needed to execute them. This highlights the lack of a systematic account of the manipulation skills required for robots in chemistry laboratories. To address this gap, we introduce TARMAC - a Taxonomy for Robot Manipulation in Chemistry - a domain-specific framework that defines and organizes the core manipulations needed in laboratory practice. Based on annotated teaching-lab demonstrations and supported by experimental validation, TARMAC categorizes actions according to their functional role and physical execution requirements. Beyond serving as a descriptive vocabulary, TARMAC can be instantiated as robot-executable primitives and composed into higher-level macros, enabling skill reuse and supporting scalable integration into long-horizon workflows. These contributions provide a structured foundation for more flexible and autonomous laboratory automation. More information is available at https://tarmac-paper.github.io/

Rachel E. Gross, L. Ilsedore Cleeves

The chemistry within a protoplanetary disk is greatly affected by external radiation from the local stellar environment. Previous work has focused on extreme radiation fields, representative of the center of something like the Orion Nebula Cluster. However, even in such environments, many disks exist at the edges of a cluster where the lower stellar density leads to radiation fields weaker by orders of magnitude compared to the center. We present new chemical models of a T-Tauri disk in the presence of a moderately increased interstellar radiation field (ISRF). Such an environment has a background UV strength of 10 to 100 times higher than the galactic average ISRF. Moderate radiation fields are among the most prevalent disk-harboring environments and have interesting implications for the chemistry of the outer disk radii. We find that the external UV radiation creates an outer ionization front that impacts the cold disk chemistry to varying degrees, depending on outer disk structure. Certain molecules like C$^+$, N$_2$H$^+$, C, and CS are more strongly impacted by the ISRF in their abundance, column density, and observable emission. Other abundant species like HCO$^+$ and CO are less affected by the external UV flux in the outer disk under such moderate UV conditions. Further, we demonstrate that the chemistry occurring in the inner tens of au is relatively unchanged, which suggests that even in moderately externally irradiated disks, the inner disk chemistry may be more similar to isolated disks like those in, e.g., the Taurus and Lupus star-forming regions.

Zhihao Zhang, Xiao-Ming Cao

Deep learning has advanced efficient chemical process simulations on the surfaces, accelerating high-throughput materials screening and rational design in heterogeneous catalysis, energy storage and conversion, and gas separation. However, the accuracy of the deep learning model generally depends on the quality of the training data. Unfortunately, precise experimental data in surface chemistry, such as adsorption energies, are scarce, while accurate quantum chemistry simulations remain computationally prohibitive for large-scale studies. Herein, we present a deep learning model of DOS Transformer for Adsorption (DOTA) for efficient surface chemistry simulations with chemical accuracy. It enables the alignment of experimental data and multi-fidelity quantum chemistry calculation data by capturing latent orbital interaction patterns based on the map between local density of states (LDOS) and adsorption energy. This minimizes the reliance on scarce high-precision training data in surface chemistry to accomplish efficient prediction of adsorption energies rivaling the high-precision experimental data, resolving the long-standing challenge of "CO puzzle". It provides a robust framework for efficient materials screening, effectively bridging the gap between computational and experimental data.

A. Ballester-Caudet, P. Campíns-Falcó, Bibiana Pérez et al.

Abstract A quantitative evaluation tool is proposed, which aims to assess optimal selection or/and testing of analytical methods. Objective criteria related to analytical performance, sustainability, environmental impact and economic cost are evaluated through the definition of penalty points divided into five different blocks, namely, figures of merit, toxicity and safety, residues, carbon footprint and economic cost. For each block, the overall qualification is scaled from 0 to 4 and it is depicted on a regular hexagonal pictogram that allows a user friendly comparison of analytical procedures. The present evaluation tool aims to be a guideline for evaluating and/or selecting analytical procedures that are in line with Green Chemistry philosophy, but also balancing the figures of merit needed for solving a given problem, safety and cost-effectiveness. Real examples have been tested.

Mohammad Khursheed Alam, Mashael Zaid Alfuhigi, Mohammad Younis Hajeer et al.

Background: Orthodontic treatment involves the use of various materials, including brackets and wires, which come into direct contact with oral tissues. Biocompatibility of these materials is crucial to ensure patient safety and treatment success. This study aims to evaluate the biocompatibility of orthodontic brackets and wires through an in-vitro investigation. Materials and Methods: Orthodontic brackets and wires commonly used in clinical practice were selected for this study. A series of in-vitro tests were conducted to assess the biocompatibility of these materials. Cell culture assays were performed to evaluate cytotoxicity, cell proliferation, and inflammatory response. In addition, scanning electron microscopy (SEM) was used to examine the surface characteristics of the materials. Results: The cytotoxicity assays revealed minimal adverse effects on cell viability, with cell viability percentages ranging from 90% to 95% for all materials tested. Cell proliferation assays demonstrated similar rates of cell growth on the surfaces of both brackets and wires. SEM analysis indicated smooth surfaces with minimal irregularities, suggesting favorable biocompatibility. Conclusion: The findings of this in-vitro study suggest that the orthodontic brackets and wires examined exhibit satisfactory biocompatibility characteristics. Minimal cytotoxicity and favorable cell proliferation indicate that these materials are well suited for use in orthodontic treatment. Further clinical studies are warranted to validate these findings and ensure the safety and efficacy of orthodontic appliances in patient care.

Hota Sankirtha, Sugumar Vimal, Alex Arockia et al.

Aeromonas hydrophila is a Gram-negative bacterium that has been linked to serious illnesses in both humans and animals. The presence of hemolysin, a virulence factor, is critical in the development of A. hydrophila-related illnesses. As a result, precise and timely detection of the hemolysin gene is critical for efficient diagnosis and prevention of many illnesses. The PCR is used in this study to detect the hemolysin gene of A. hydrophila in a novel, fast, and highly sensitive one-step technique. Specific primers were constructed to amplify a conserved area within the hemolysin gene to achieve both specificity as well as sensitivity. The PCR assay was rigorously optimized, taking temperature, primer concentration, and reaction time into account, in order to maximize the efficiency and reliability of this method. In conclusion, this method’s simplicity, sensitivity, and specificity make it highly promising for regular diagnostic applications. Its application would allow for the early detection of A. hydrophila infections, allowing for more effective treatment and control methods.

Shenoi Pratima R, Bhongade Bhoomendra A, Shingane Shrikant A et al.

Herbal extracts have evoked interest owing to the small number of terpenoids and phenolic compounds, which impart antimicrobial, antioxidant, and anti-inflammatory properties. The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and minimum fungicidal concentration (MFC) of four herbal extracts (lemon grass oil, basil oil, peppermint oil, and Obicure tea extract) against endodontic pathogens along with the MIC: MBC/MFC ratio were evaluated. The antimicrobial activity by detecting the MIC of three essential oils and tea extract was evaluated against eight common endodontic pathogens by the broth dilution method, while MBC was detected by subculturing onto blood agar from the first –three to five tubes from the MIC dilution tubes (showing no turbidity), which were plated on blood agar. All herbal extracts proved to be effective antimicrobials against tested endodontic pathogens. Basil oil had a bacteriostatic effect on all the organisms (P < 0.05). Mint oil showed bacteriostatic activity on Enterococcus (E.) faecalis and Peptostreptococcus (P > 0.05). Tea extract had a bacteriostatic effect (P > 0.05) against all tested microbes except Actinomyces, Lactobacilli, Staphylococcus (S.) aureus, and Fusobacterium (F.) nucleatum. Lemon grass oil had a bactericidal effect against all the organisms and a bacteriostatic effect against Peptostreptococcus (P > 0.05). It can be concluded that basil oil showed a strong bactericidal effect on the test organisms. The MIC for the organisms ranged from 0.2 to 50 μg/ml.

Pratiksha Munjewar, Ranjana Sharma, Gauri Chandrashekhar Mahakalkar et al.

Background: Chronic kidney disease (CKD) is a global health issue, affecting 13.4% of the population. Many CKD patients progress to end-stage renal disease, necessitating lifelong renal replacement therapy like dialysis. Hemodialysis significantly alters patients’ lifestyles, causing social isolation, work–life changes, financial strain, and family role shifts. These challenges lead to fatigue, pain, depression, and sleep disturbances, severely impacting patients’ quality of life and daily activities. Among these symptoms, fatigue is one of the most common and disruptive. Aim: To assess the effectiveness of video-assisted teaching on knowledge regarding the management of post-hemodialysis fatigue among patients attending hemodialysis. Material and Method: This study was based on a quantitative research approach and one group pretest and post-test research design. This study includes 200 patients attending hemodialysis from Acharya Vinoba Bhave Rural Hospital Wardha with the nonprobability convenience sampling technique. A questionnaire was used to assess the knowledge of the patient. Result: After the educational program, the knowledge score improved significantly. The poor category decreased to 50 participants (25.0%), while the average category dropped to participants (5.0%). The good category increased substantially to 110 participants (55.5%), and 30 participants (15.0%) achieved an excellent level of knowledge. Conclusion: Results suggest that after applying intervention, the knowledge regarding post-hemodialysis fatigue is increased in patients attending hemodialysis.

Mohammed Ibrahim Mathar, Manish Chadha, AlMoataz Mohamed Amin Soliman et al.

Objectives: To evaluate the Vickers hardness and flexural strength of computer-aided design/computer-aided manufacture (CAD/CAM) milled, 3D-printed, and traditional heat-polymerized denture base resins used in computer-aided design and manufacture. Materials and Methods: A total of 60 samples were fabricated from CAD/CAM milled resin (PMMA dental material-Ruthinium disc, Badia Polesine (Rovigo) Italy), CAD/CAM 3D-printed resin (NextDent Denture 3D+, Soesterberg, The Netherlands) and conventional heat-polymerized (HP) denture base resin (DBR) (Triplex hot Ivoclar-Vivadent, Liechtenstein). Based on the three different denture base resin materials (n = 10/material) (30/flexural strength and 30/microhardness), the samples were split into six groups. The 3-point bending test was used to assess flexural strength, while Vickers microhardness test was used to assess surface hardness. The acquired data was statistically assessed. Results: CAD/CAM milled resins showed appreciably greater values for both the flexural strength and surface hardness, followed by conventional HP denture base resin and CAD/CAM 3D-printed resin. Pairwise comparison for Flexural Strength and Vickers microhardness revealed significant differences between groups. Conclusion: CAD/CAM milled resins had the highest surface hardness and flexural strength compared to Conventional HP denture base resin and CAD/CAM 3D-printed resin.

Jain Sulabh, Sarah, Mishra Shweta et al.

BackgroundDental imaging plays a crucial role in diagnosis and treatment planning, with cone-beam computed tomography (CBCT) and medical computed tomography (CT) being two common modalities. This study aims to compare the radiation doses associated with CBCT and medical CT imaging in dental applications to assess their relative safety and efficacy. Materials and MethodsWe conducted a retrospective study using data from 100 patients who underwent both CBCT and medical CT scans for dental purposes. The radiation doses were measured in terms of dose-length product (DLP) for medical CT and dose-area product (DAP) for CBCT. The effective dose (ED) was calculated using appropriate conversion factors. Patient demographics, scan parameters, and radiation doses were recorded and analyzed. ResultsThe results indicated that the mean DLP for medical CT scans was 220 mGycm, whereas the mean DAP for CBCT scans was 150 mGycm². The corresponding mean effective doses for medical CT and CBCT were 2.5 mSv and 1.8 mSv, respectively. The radiation dose from CBCT was found to be approximately 28% lower than that from medical CT. ConclusionThis study demonstrates that CBCT imaging for dental applications results in significantly lower radiation doses compared to medical CT. While both modalities provide valuable diagnostic information, the choice of imaging technique should consider the balance between diagnostic quality and radiation exposure, especially for pediatric and high-risk patients. Dental practitioners should be aware of the potential dose reduction benefits associated with CBCT when appropriate for the clinical scenario.

Mona Aljefiri, Mona Alhabsh, Manar Alabbasi et al.

Background: Artificial intelligence (AI) and robotics have gained much attention during the last decade in the medical field, and they will probably affect the practice of the next generation of healthcare providers. Methods: This cross-sectional study used an online questionnaire to assess students’ and faculty’s prior knowledge and perceptions of AI and robotics. It was conducted at King Abdulaziz University, Jeddah. The sample of the present study includes 374 participants. Data was collected, processed, and statistically analyzed using IBM SPSS Statistics Data Editors. The level of statistical significance was assumed to be a P value >0.05, which indicates a non-significant difference. Results: Most participants were healthcare students (87%) aged 18–24 (84%). Overall, both students and faculty were moderately familiar with AI and robotics in medicine (30.7% and 31.3%, respectively). Both students and faculty wanted to incorporate AI and robotics into their medical curriculum (63.2% and 81.2%, respectively). They saw AI as already present in the field of surgery (37.4% and 45.8%, respectively) and further implemented in the same field prospectively, too (38.3% and 52.1%, respectively). Many participants believed that AI would only be integrated into healthcare and operated by a specialist (79%); however, the majority still favored the physician’s opinion over AI (63%). Conclusion: Most healthcare students and faculty recognize the significance of AI and are excited to engage. AI and robotics should be given ample consideration in the education curriculum by enhancing continual training programs for faculty to conduct AI and robotics courses.

Chang Liao, Yemin Yu, Yu Mei et al.

In recent years, Large Language Models (LLMs) have achieved significant success in natural language processing (NLP) and various interdisciplinary areas. However, applying LLMs to chemistry is a complex task that requires specialized domain knowledge. This paper provides a thorough exploration of the nuanced methodologies employed in integrating LLMs into the field of chemistry, delving into the complexities and innovations at this interdisciplinary juncture. Specifically, our analysis begins with examining how molecular information is fed into LLMs through various representation and tokenization methods. We then categorize chemical LLMs into three distinct groups based on the domain and modality of their input data, and discuss approaches for integrating these inputs for LLMs. Furthermore, this paper delves into the pretraining objectives with adaptations to chemical LLMs. After that, we explore the diverse applications of LLMs in chemistry, including novel paradigms for their application in chemistry tasks. Finally, we identify promising research directions, including further integration with chemical knowledge, advancements in continual learning, and improvements in model interpretability, paving the way for groundbreaking developments in the field.

Zihan Zhao, Da Ma, Lu Chen et al.

Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require training and tuning for specific tasks. A more generic and efficient solution would be an AI model that could address many tasks and support free-form dialogue in the broad field of chemistry. In its utmost form, such a generalist AI chemist could be referred to as Chemical General Intelligence. Large language models (LLMs) have recently logged tremendous success in the general domain of natural language processing, showing emerging task generalization and free-form dialogue capabilities. However, domain knowledge of chemistry is largely missing when training general-domain LLMs. The lack of such knowledge greatly hinders the performance of generalist LLMs in the field of chemistry. To this end, we develop ChemDFM, a pioneering LLM for chemistry trained on 34B tokens from chemical literature and textbooks, and fine-tuned using 2.7M instructions. As a result, it can understand and reason with chemical knowledge in free-form dialogue. Quantitative evaluations show that ChemDFM significantly surpasses most representative open-source LLMs. It outperforms GPT-4 on a great portion of chemical tasks, despite the substantial size difference. We have open-sourced the inference codes, evaluation datasets, and model weights of ChemDFM on Huggingface (https://huggingface.co/OpenDFM/ChemDFM-v1.0-13B).

Vincent Fan, Yujie Qian, Alex Wang et al.

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of \ours attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.

Mridula Kuppa, Roger Ghanem, Marco Panesi

This work presents a novel framework for physically consistent model error characterization and operator learning for reduced-order models of non-equilibrium chemical kinetics. By leveraging the Bayesian framework, we identify and infer sources of model and parametric uncertainty within the Coarse-Graining Methodology across a range of initial conditions. The model error is embedded into the chemical kinetics model to ensure that its propagation to quantities of interest remains physically consistent. For operator learning, we develop a methodology that separates time dynamics from other input parameters. Karhunen-Loeve Expansion (KLE) is employed to capture time dynamics, yielding temporal modes, while Polynomial Chaos Expansion (PCE) is subsequently used to map model error and input parameters to KLE coefficients. The proposed model offers three significant advantages: i) Separating time dynamics from other inputs ensures stability of chemistry surrogate when coupled with fluid solvers; ii) The framework fully accounts for model and parametric uncertainty, enabling robust probabilistic predictions; iii) The surrogate model is highly interpretable, with visualizable time modes and a PCE component that facilitates analytical calculation of sensitivity indices. We apply this framework to O2-O chemistry system under hypersonic flight conditions, validating it in both a 0D adiabatic reactor and coupled simulations with a fluid solver in a 1D shock case. Results demonstrate that the surrogate is stable during time integration, delivers physically consistent probabilistic predictions accounting for model and parametric uncertainty, and achieves maximum relative error below 10%. This work represents a significant step forward in enabling probabilistic predictions of non-equilibrium chemistry with coupled fluid solvers, offering a physically accurate approach for hypersonic flow predictions.