Key Structural Components Research Articles

Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models.

Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective invivo detection techniques to provide a deeper understanding. This study presents photoacoustic spectral analysis improved by machine learning as a promising non-invasive diagnostic method, focusing on exploring collagen as a salient biomarker. Murine model experiments revealed more profound associations of collagen with other cancer components than in normal tissues. Moreover, an optimal set of feature wavelengths was identified by a genetic algorithm for enhanced diagnostic performance, among which 75% were from collagen-dominated absorption wavebands. Using optimal spectra, the diagnostic algorithm achieved 72% accuracy, 66% sensitivity, and 78% specificity, surpassing full-range spectra by 6%, 4%, and 8%, respectively. The proposed photoacoustic methods examine the feasibility of offering valuable biochemical insights into existing techniques, showing great potential for early-stage cancer detection.

Analyzing vibration transmission through cantilever systems using biomechanical and impact-responsive metamaterial structures

Vibration control in cantilever systems is a critical challenge in various engineering applications, where unwanted vibrations can lead to structural fatigue, reduced performance, and potential failure. This study investigates the effects of integrating biomechanical and impact-responsive metamaterials into cantilever systems to mitigate vibration transmission. The metamaterials, characterized by their adaptive stiffness and energy-absorbing properties, are strategically embedded in key structural components such as the arms, joints, and base. Through experimental analysis, this work assesses the reduction in vibration amplitude, shifts in natural frequency, enhanced damping capacity, energy absorption during impact, and strain reduction at critical points. The results show that the metamaterial-enhanced system achieves significant reductions in vibration amplitude, up to 40%, and increases in natural frequency by over 30%, minimizing the risk of resonance. Additionally, the damping ratio is improved by as much as 53%, while the energy absorption during impact is increased by up to 26%. Strain reduction at critical points reaches 24%, contributing to improved mechanical resilience. These findings demonstrate the potential of biomechanical and impact-responsive metamaterials in enhancing the dynamic performance of cantilever systems, offering a new approach to vibration mitigation in engineering applications.

Research on Hydraulic Characteristics of Water Leakage Phenomenon of Waterproof Hammer Air Valve in Water Supply Pressure Pipeline Based on Sustainable Utilization of Water Resources in Irrigation Areas

To investigate the causes of water leakage in the waterproof hammer air valve and its impact on sustainable water resource management, the DN100 waterproof hammer air valve was taken as the research object. By using the overset grid solution method of ANSYS Fluent 2021 R1 software, the flow field simulation of the waterproof hammer air valve was carried out. The transient action during the ascent phase of the key structural component floating ball, and the velocity and pressure distribution of the flow field inside the air valve are analyzed. The results showed that by giving different inlet flow velocities, the normal flow velocity range for the floating ball to float up was below 35 m/s and above 50 m/s. When the inlet flow velocity was between 35 m/s and 50 m/s, the growth rate of the pressure difference above and below the floating ball increased from 1.48% to 5.79% and then decreased to 0.4%. The floating ball would not be able to float up due to excessive outlet pressure above, which would cause the DN100 waterproof hammer air valve to leak water and fail to provide water hammer protection. When the inlet flow rate is 5 m/s, the velocity and pressure inside the valve body increase with time during the upward movement of the floating ball inside the waterproof hammer air valve and tend to stabilize at 400 ms. Through the generated pressure and velocity cloud maps, it can be observed that the location of maximum pressure is at the bottom of the buoy, directly below the floating ball, and at the narrow channels on both sides of the outflow domain. The location of the maximum velocity is at the small inlet of the bottom of the buoy. When the inlet speed of the valve is constant, a large amount of water flow is blocked by the floating ball, reducing the flow velocity and forming partial backflow below the floating ball, with an obvious vortex phenomenon. A small portion of the water flow passes through the air valve at a high velocity from both ends of the channel, and the water flow below the floating ball is in an extremely unstable state under the impact of high-speed water flow, resulting in a large gradient of water flow velocity passing through the valve. The research results not only help to improve the operational efficiency of water resource management systems but also reduce unnecessary water resource waste, thereby supporting the goal of sustainable water resource management.

Enterprise Data Governance : A Comprehensive Framework for Ensuring Data Integrity, Security, and Compliance in Modern Organizations

Enterprise data governance has emerged as a critical function for organizations seeking to maximize the value of their data assets while mitigating risks associated with data management. This article presents a comprehensive framework for implementing effective data governance strategies in modern enterprises. It examines the key components of robust governance structures, including clear policies, well-defined roles and responsibilities, and the establishment of data quality standards. The article explores methods for ensuring regulatory compliance and enhancing data security, emphasizing the importance of risk management and data protection measures. By analyzing case studies across various industries, the article identifies best practices and common challenges in data governance implementation. Additionally, it investigates the impact of emerging technologies such as artificial intelligence and blockchain on data governance practices. The findings underscore the necessity of a holistic approach to data governance that aligns with organizational objectives, fosters a data-driven culture, and adapts to evolving regulatory landscapes. This article contributes to the growing body of knowledge on enterprise data management and provides practical insights for organizations striving to improve their data governance capabilities in an increasingly complex digital environment.

Modeling the Dynamic Properties of Multi-Layer Glass Fabric Sandwich Panels.

Sandwich panels are key components of many lightweight structures. They are often subjected to time-varying loads, which can cause various types of vibrations that adversely affect the functionality of the structure. That is why it is of such importance to predict the dynamic properties of both the panels and the structures made of them at the design stage. This paper presents finite element modeling of the dynamic properties (i.e., natural frequencies, mode shapes, and frequency response functions) of sandwich panels made of glass fabric impregnated with phenolic resin. The model reproducing the details of the panel structure was built using two-dimensional, quadrilateral, isoparametric plane elements. Afterwards, the model was subjected to an updating procedure based on experimentally determined frequency response functions. As a result, the average relative error for natural frequencies achieved numerically was 5.0%. Finally, a cabinet model consisting of the analyzed panels was built and experimentally verified. The relative error between the numerically and experimentally obtained natural frequencies was on average 5.9%.

Photokatalytische PPh3‐vermittelte Synthese von C3‐funktionalisierten Indolen über radikalische Anellierung von Nitroaromaten und Alkenen

AbstractOxidativ erzeugte Phosphin‐Radikalkationen sind reaktive Intermediate, die über Deoxygenierungsprozesse zur Erzeugung von kohlenstoff‐ und heteroatomzentrierten Radikalen verwendet werden können. Solche P‐Radikalkationen lassen sich leicht durch Ein‐Elektronentransfer‐Oxidation mit einem Redoxkatalysator erzeugen. Günstige und kommerziell erhältliche Nitroaromaten sind ideale Stickstoffquellen zum Aufbau organischer Amine und stickstoffhaltiger Heterozyklen. Die Aktivierung von Nitroaromaten mit Phosphinen wurde bereits in ionischen Prozessen erreicht, die jedoch spezielle Phosphor‐Nukleophile und hohe Temperaturen erfordern. In dieser Arbeit wird über einen alternativen Weg der Nitroaktivierung berichtet, der über einen radikalischen Prozess verläuft. Diese Strategie führt zu offenschaligen Zwischenprodukten, die eine interessante, unerforschte Reaktivität zeigen. Dies wird durch die Entwicklung einer wirtschaftlichen und hoch effizienten Synthese wertvoller Indolderivate durch photokatalytische PPh3‐vermittelte Anellierung von Nitroaromaten mit Alkenen dokumentiert. Die Sequenz weist eine große Toleranz gegenüber funktionellen Gruppen auf. Das Verfahren ermöglicht die Aktivierung von Nitroaromaten bei Raumtemperatur, wobei eine doppelte C−H‐Bindungsfunktionalisierung von Alkenen erreicht wird, um einen schnellen Zugang zu C3‐funktionalisierten Indolen zu bieten, die Schlüsselstrukturkomponenten verschiedener Naturstoffe und Arzneimittel darstellen. Mechanistische Studien, die durch DFT‐Berechnungen weiter unterstützt werden, deuten darauf hin, dass ein Nitrosoaryl‐Radikalkation eine Schlüsselrolle im Anellierungsprozess spielt.

Photocatalytic PPh3-Mediated Synthesis of C3-Functionalized Indoles via Radical Annulation of Nitroarenes and Alkenes.

Oxidatively generated phosphine radical cations are reactive intermediates that can be used for the generation of carbon and heteroatom centered radicals via deoxygenation processes. Such P-radical cations can readily be generated via single electron transfer oxidation using a redox catalyst. Cheap and commercially available nitroarenes are ideal nitrogen sources for the construction of organic amines and N-containing heterocycles. Activation of nitroarenes with phosphines has been achieved in the ionic mode, which requires specially designed P-nucleophiles and high temperatures. Herein, we report an alternative mode of nitro activation that proceeds via a radical process. The radical strategy leads to open shell intermediates that show interesting unexplored reactivity. This is documented by the development of an economic and highly efficient synthesis of valuable indole derivatives through photocatalytic PPh3-mediated annulation of nitroarenes with alkenes showing large functional group tolerance. The method allows room-temperature activation of nitroarenes and a double C-H bond functionalization of alkenes is achieved to provide rapid access to C3-functionalized indoles, which are key structural components of diverse natural and drug molecules. Experimental mechanistic studies that are further supported by DFT calculations indicate that a nitrosoarene radical cation plays a key role in the annulation process.

3D bioprinting of an intervertebral disc tissue analogue with a highly aligned annulus fibrosus via suspended layer additive manufacture.

Intervertebral disc (IVD) function is achieved through integration of its two component regions: the nucleus pulposus (NP) and the annulus fibrosus (AF). The NP is soft (0.3-5 kPa), gelatinous and populated by spherical NP cells in a polysaccharide-rich extracellular matrix (ECM). The AF is much stiffer (∼100 kPa) and contains layers of elongated AF cells in an aligned, fibrous ECM. Degeneration of the disc is a common problem with age being a major risk factor. Progression of IVD degeneration leads to chronic pain and can result in permanent disability. The development of therapeutic solutions for IVD degeneration is impaired by a lack ofin vitromodels of the disc that are capable of replicating the fundamental structure and biology of the tissue. This study aims to investigate if a newly developed suspended hydrogel bioprinting system (termed SLAM) could be employed to fabricate IVD analogues with integrated structural and compositional features similar to native tissue. Bioprinted IVD analogues were fabricated to recapitulate structural, morphological and biological components present in the native tissue. The constructs replicated key structural components of native tissue with the presence of a central, polysaccharide-rich NP surrounded by organised, aligned collagen fibres in the AF. Cell tracking, actin and matrix staining demonstrated that embedded NP and AF cells exhibited morphologies and phenotypes analogous to what is observedin vivowith elongated, aligned AF cells and spherical NP cells that deposited HA into the surrounding environment. Critically, it was also observed that the NP and AF regions contained a defined cellular and material interface and segregated regions of the two cell types, thus mimicking the highly regulated structure of the IVD.

Enhancing abstractive summarization of scientific papers using structure information

Abstractive summarization of scientific papers has always been a research focus, yet existing methods face two main challenges. First, most summarization models rely on Encoder-Decoder architectures that treat papers as sequences of words, thus fail to fully capture the structured information inherent in scientific papers. Second, existing research often use keyword mapping or feature engineering to identify the structural information, but these methods struggle with the structural flexibility of scientific papers and lack robustness across different disciplines. To address these challenges, we propose a two-stage abstractive summarization framework that leverages automatic recognition of structural functions within scientific papers. In the first stage, we standardize chapter titles from numerous scientific papers and construct a large-scale dataset for structural function recognition. A classifier is then trained to automatically identify the key structural components (e.g., Background, Methods, Results, Discussion), which provides a foundation for generating more balanced summaries. In the second stage, we employ Longformer to capture rich contextual relationships across sections and generating context-aware summaries. Experiments conducted on two domain-specific scientific paper summarization datasets demonstrate that our method outperforms advanced baselines, and generates more comprehensive summaries. The code and dataset can be accessed at https://github.com/tongbao96/code-for-SFR-AS.

Seismic behavior of composite shear walls with post-casting UHPC boundary elements and CRB600H steel bars

This study investigates the seismic behavior of composite shear walls reinforced with post-casting Ultra-High Performance Concrete (UHPC) boundary elements and Colded-Rolled Ribbed Bars (CRB600H). The research aims to explore the synergistic effects of UHPC and CRB600H bars on seismic performance. Six shear wall specimens with a shear span ratio of 1.56 were subjected to cyclic loading to evaluate their failure modes, crack patterns, hysteresis behavior, stiffness degradation, ductility, residual deformation, and energy dissipation capacity. The results demonstrated that the use of CRB600H bars in boundary elements of normal concrete specimens improved post-yield stiffness and reduced residual deformation. Conversely, incorporating UHPC in boundary elements enhanced load-bearing capacity and crack control but slightly reduced ductility due to the weaker interfacial connection between UHPC and the foundation. Additionally, CRB600H bars in the web region improved load-bearing capacity while maintaining ductility comparable to HRB400 bars. The findings suggest that substituting CRB600H for HRB400 in shear-dominant regions can enhance the seismic performance of shear walls. Finally, the multi-layer shell element numerical model validated by the experimental results was encouraged to conduct parametric analysis in the future. This research supports the development of more resilient building structures by integrating high-performance materials in key structural components.

Malate dehydrogenase: a story of diverse evolutionary radiation.

Malate dehydrogenase (MDH) is a ubiquitous enzyme involved in cellular respiration across all domains of life. MDH's ubiquity allows it to act as an excellent model for considering the history of life and how the rise of aerobic respiration and eukaryogenesis influenced this evolutionary process. Here, we present the diversity of the MDH family of enzymes across bacteria, archaea, and eukarya, the relationship between MDH and lactate dehydrogenase (LDH) in the formation of a protein superfamily, and the connections between MDH and endosymbiosis in the formation of mitochondria and chloroplasts. The development of novel and powerful DNA sequencing techniques has challenged some of the conventional wisdom underlying MDH evolution and suggests a history dominated by gene duplication, horizontal gene transfer, and cryptic endosymbiosis events and adaptation to a diverse range of environments across all domains of life over evolutionary time. The data also suggest a superfamily of proteins that do not share high levels of sequential similarity but yet retain strong conservation of core function via key amino acid residues and secondary structural components. As DNA sequencing and 'big data' analysis techniques continue to improve in the life sciences, it is likely that the story of MDH will continue to refine as more examples of superfamily diversity are recovered from nature and analyzed.

Machine Learning-Assisted Prediction of Stress Corrosion Crack Growth Rate in Stainless Steel

Stainless-steel is extensively utilized in the key structural components of the main equipment in the nuclear island of pressurized water reactor nuclear power plants. The operational experience of nuclear power plants demonstrates that stress corrosion is one of the significant factors influencing the long-term safe operation of stainless steel in the high-temperature water of pressurized water reactor nuclear power plants. This study is based on the stress corrosion crack growth rate data of 316SS and 304SS stainless steel in the simulated primary water environment of pressurized water reactor nuclear power plants. Data mining and modeling were conducted using multiple machine learning algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), and the Sharpley Additive explanation (SHAP) method was employed to analyze the interpretability of the model. The results indicate that the stress corrosion crack growth rate prediction model based on XGBoost outperforms other models in all assessment indicators. Compared with empirical equations, XGBoost exhibits high flexibility and excellent data-driven learning capabilities. In the test set, 90% of the prediction errors are within the range of experimental values, with the maximum error multiple being 2.5, which significantly improves the prediction accuracy. Moreover, the distribution of SHAP values is consistent with the theoretical study of the stress corrosion behavior of stainless-steel, effectively reflecting the impact of cold working, temperature, and stress intensity factor on the stress corrosion crack growth rate, thereby proving the reliability of the model’s prediction results. The achievements of this study hold significant reference value and application prospects for the prediction of the stress corrosion behavior of stainless-steel in a high-temperature and high-pressure water environment of pressurized water reactor nuclear power plants.

Tristetraprolin affects invasion-associated genes expression and cell motility in triple-negative breast cancer model.

Tristetraprolin (TTP) is an RNA-binding protein that negatively regulates its target mRNAs and has been shown to inhibit tumor progression and invasion. Tumor invasion requires precise regulation of cytoskeletal components, and dysregulation of cytoskeleton-associated genes can significantly alter cell motility and invasive capability. Several genes, including SH3PXD2A, SH3PXD2B, CTTN, WIPF1, and WASL, are crucial components of the cytoskeleton reorganization machinery and are essential for adequate cell motility. These genes are also involved in invasion processes, with SH3PXD2A, SH3PXD2B, WIPF1, and CTTN being key components of invadopodia-specialized structures that facilitate invasion. However, the regulation of these genes is not well understood. This study demonstrates that ectopic expression of TTP in MDA-MB-231 cells leads to decreased mRNA levels of CTTN and SH3PXD2A, as well as defects in cell motility and actin filament organization. Additionally, doxorubicin significantly increases TTP expression and reduces the mRNA levels of cytoskeleton-associated genes, enhancing our understanding of how doxorubicin may affect the transcriptional profile of cells. However, doxorubicin affects target mRNAs differently than TTP ectopic expression, suggesting it may not be the primary mechanism of doxorubicin in breast cancer (BC) treatment. High TTP expression is considered as a positive prognostic marker in multiple cancers, including BC. Given that doxorubicin is a commonly used drug for treating triple-negative BC, using TTP as a prognostic marker in this cohort of patients might be limited since it might be challenging to understand if high TTP expression occurred due to the favorable physiological state of the patient or as a consequence of treatment.

Michael Acceptor Pyrrolidone Derivatives and Their Activity against Diffuse Large B-cell Lymphoma.

This study aimed to design and evaluate the efficacy of pyrrolidone derivatives as potential therapeutic agents against diffuse large B-cell lymphoma (DLBCL), a common and heterogeneous malignancy of the adult lymphohematopoietic system. Given the limitations of current therapies, there is a pressing need to develop new and effective drugs for DLBCL treatment. A series of pyrrolidone derivatives were synthesized, and their antitumor activities were assessed, particularly against DLBCL cell lines. Structure-activity relationship (SAR) analysis was conducted to identify key structural components essential for activity. The most promising compound, referred to as compound 7, was selected for further mechanistic studies. The expression levels of relevant mRNA and protein were detected by RT-qPCR and Western blotting, and the expression of mitochondrial membrane potential and ROS was detected using flow cytometry for further assessment of cell cycle arrest and apoptosis. The compound 7 exhibited good antitumor activity among the synthesized derivatives, specifically in DLBCL cell lines. SAR analysis highlighted the critical role of α, β-unsaturated ketones in the antitumor efficacy of these compounds. Mechanistically, compound 7 was found to induce significant DNA damage, trigger an inflammatory response, cause mitochondrial dysfunction, and disrupt cell cycle progression, ultimately leading to apoptosis of DLBCL cells. The compound 7 has good antitumor activity and can induce multiple cellular mechanisms leading to cancer cell death. These findings warrant further investigation of the compound 7 as a potential therapeutic agent for DLBCL.

New Analytical Solutions for Free Vibration of Cracked Functionally Graded Rectangular Plates

In aerospace engineering, the existence of cracks highly alters the mechanical behaviors of structural components. As a new class of key structural components, functionally graded material (FGM) rectangular plates with an interior/side crack are focused on in this study. New analytical free vibration solutions are put forward for such cracked FGM plates. First, a cracked plate is decomposed into several subplates with the necessary continuity conditions to address the physical discontinuity issue caused by the crack. The challenge then becomes deriving analytical free vibration solutions for each subplate. To accomplish this task, a symplectic superposition method (SSM) is developed to deal with FGM rectangular plates with various boundary conditions, including the complex continuity conditions. The SSM eliminates the need for a predefined solution form, and its solution procedure, including variable separation and eigen expansion, is mathematically rigorous. Afterward, the analytical solutions obtained via the SSM are integrated to serve as the final free vibration solution of the cracked FGM plate. Comprehensive results based on the obtained analytical solutions, including natural frequencies and vibration modes, are presented and validated. The solutions are further used to investigate the effects of some important parameters, such as crack length and crack location.

Surface effects on functional amyloid formation.

Functional amyloids formed by the protein FapC in Pseudomonas bacteria are key structural components of Pseudomonas biofilms, which mediate chronic infections and also contribute to antimicrobial resistance. Here, we combine kinetic experiments with mechanistic modelling to probe the role of surfaces in FapC functional amyloid formation. We find that nucleation of new fibrils is predominantly heterogeneous in vitro, being catalysed by reaction vessel walls but not by the air/water interface. Removal of such interfaces by using microdroplets greatly slows heterogeneous nucleation and reveals a hitherto undetected fibril surface-catalysed "secondary nucleation" reaction step. We tune the degree of catalysis by varying the interface chemistry of the reaction vessel and by adding nanoparticles with tailored surface properties that catalyse fibril nucleation. In so doing, we discover that the rate of nucleation is controlled predominantly by the strength with which FapC binds to the catalytic sites on the interface, and by its surface area. Surprisingly, neither primary nucleation rate nor catalytic site binding strength appear closely correlated to the charge and hydrophilicity of the interface. This indicates the importance of considering experimental design in terms of surface chemistry of the reaction container while also highlighting the notion that fibril nucleation during protein aggregation is a heterogeneous process.

Evaluation of the Leishmania Inositol Phosphorylceramide Synthase as a Drug Target Using a Chemical and Genetic Approach.

The lack of effective vaccines and the development of resistance to the current treatments highlight the urgent need for new anti-leishmanials. Sphingolipid metabolism has been proposed as a promising source of Leishmania-specific targets as these lipids are key structural components of the eukaryotic plasma membrane and are involved in distinct cellular events. Inositol phosphorylceramide (IPC) is the primary sphingolipid in the Leishmania species and is the product of a reaction mediated by IPC synthase (IPCS). The antihistamine clemastine fumarate has been identified as an inhibitor of IPCS in L. major and a potent anti-leishmanial in vivo. Here we sought to further examine the target of this compound in the more tractable species L. mexicana, using an approach combining genomic, proteomic, metabolomic and lipidomic technologies, with molecular and biochemical studies. While the data demonstrated that the response to clemastine fumarate was largely conserved, unexpected disturbances beyond sphingolipid metabolism were identified. Furthermore, while deletion of the gene encoding LmxIPCS had little impact in vitro, it did influence clemastine fumarate efficacy and, importantly, in vivo pathogenicity. Together, these data demonstrate that clemastine does inhibit LmxIPCS and cause associated metabolic disturbances, but its primary target may lie elsewhere.

Predicting glass transition temperature of polymers by combining molecular dynamics simulations and machine learning techniques

Polymeric materials, valued for their cost-effectiveness and ease of processing, play a pivotal role in diverse applications. Among them, solution polymerized styrene butadiene rubber (SSBR) stands out with low rolling and wet skid resistance, ideal for eco-friendly tire. The glass transition temperature (Tg) is an important parameter in determining the performance of SSBR, which is time-consuming to test with conventional experiments. In recent years, machine learning (ML) has emerged as a useful tool in materials science. In this study, all-atom molecular dynamics (AAMD) simulation provided the foundational training data. Subsequently, ML techniques are utilized to efficiently predict SSBR’s Tg based on its content of four key structural components. To address the challenge of limited sample sizes, the innovative integration of Ordinary Kriging (OK) from geostatistics and Nearest Neighbor Interpolation (NNI) was employed to compute label values for interpolation points, which surpasses previous studies relying solely on NNI. Comparative analysis of four ML models (XGBoost, Ridge, LASSO, and SVR) reveals XGBoost’s superior performance in handling this nonlinear problem, with consistently high R2 and low RMSE scores. Through 900 repeated experiments, the model demonstrates robustness, maintaining an average R2 of approximately 0.9703. Ultimately, the novel integrated ML approach (NNI-OK-XGBoost) was proposed. Furthermore, four new combinations of structural unit content were evaluated using both ML predictions and AAMD simulations. The validation confirms the efficiency and accuracy of this method in predicting SSBR’s Tg across varying composition ratios within a specified composition space. This work presents a viable solution for accurately and rapidly predicting the Tg of polymeric materials, particularly in scenarios with limited sample sizes.

Novel aminoquinazolinone-based colorimetric chemosensor for highly selective recognition of gamma-hydroxybutyric acid (GHB) in spiked beverages

Gamma-hydroxybutyric acid (GHB) is one of the substances frequently used in sexual assault that has raised significant concerns in society. Its unique biological properties and the difficulty in detecting it when added to beverages are the main reasons for its misuse. In this work, we designed aminoquinazolinone derivatives with a nitro group as the key structural component, providing exceptional specificity for GHB. These compounds successfully overcome challenges related to interference from structurally similar substances and from compounds commonly found in beverages, with a specific detection mechanism for both the hydroxyl and carboxylate groups present in the GHB structure. The compounds enable facile and rapid measurement without the need for pretreatment steps prior to the GHB measurement, with the result observed by naked eye. The chemosensor probe for GHB detection developed demonstrates the potential of a solution and paper-based analysis formats.

Preparation, Deformation Behavior and Irradiation Damage of Refractory Metal Single Crystals for Nuclear Applications: A Review.

Refractory metal single crystals have been applied in key high-temperature structural components of advanced nuclear reactor power systems, due to their excellent high-temperature properties and outstanding compatibility with nuclear fuels. Although electron beam floating zone melting and plasma arc melting techniques can prepare large-size oriented refractory metals and their alloy single crystals, both have difficulty producing perfect defect-free single crystals because of the high-temperature gradient. The mechanical properties of refractory metal single crystals under different loads all exhibit strong temperature and crystal orientation dependence. Slip and twinning are the two basic deformation mechanisms of refractory metal single crystals, in which low temperatures or high strain rates are more likely to induce twinning. Recrystallization is always induced by the combined action of deformation and annealing, exhibiting a strong crystal orientation dependence. The irradiation hardening and neutron embrittlement appear after exposure to irradiation damage and degrade the material properties, attributed to vacancies, dislocation loops, precipitates, and other irradiation defects, hindering dislocation motion. This paper reviews the research progress of refractory metal single crystals from three aspects, preparation technology, deformation behavior, and irradiation damage, and highlights key directions for future research. Finally, future research directions are prospected to provide a reference for the design and development of refractory metal single crystals for nuclear applications.