Search Results (375)

The genus Anisosciadium, belonging to the Apiaceae family, has been traditionally recognized for its anti-inflammatory, antioxidant, and antimicrobial properties. However, scientific research on this genus is still limited, highlighting the need for a comprehensive review of its chemical composition and pharmacological characteristics. A comprehensive compilation of data was conducted using major databases such as Google Scholar, Research Gate, Web of Science, Scopus, and ScienceDirect. In this review, we collected and organized the available information of identified compounds from different species of the genus Anisosciadium, covering the literature from 2003 to 2024. In total, 64 phytoconstituents were detected. The findings suggest that the traditional therapeutic properties of Anisosciadium are well supported by the reported pharmacological activities from previous studies. Notably, these studies highlight its antioxidant, antibacterial, and cytotoxic effects, emphasizing the potential of this genus in the development of new therapeutic agents. Nonetheless, the lack of comparative studies among Anisosciadium species and the scarcity of in vivo studies and clinical trials limit the full realization of its therapeutic potential. Specifically, comparative studies could be crucial in identifying species with unique chemical profiles and understanding how variations in secondary metabolite compositions may influence their pharmacological activities. Full article

One major challenge in cellular neuroscience is to elucidate how the accurate alignment of presynaptic release sites with postsynaptic densely clustered ligand-gated ion channels at chemical synapses is achieved upon synapse assembly. The clustering of neurotransmitter receptors at postsynaptic sites is a key moment of synaptogenesis and determinant for effective synaptic transmission. The number of the ionotropic neurotransmitter receptors at these postsynaptic sites of both excitatory and inhibitory synapses is variable and is regulated by different mechanisms, thus allowing the modulation of synaptic strength, which is essential to tune neuronal network activity. Several well-regulated processes seem to be involved, including lateral diffusion within the plasma membrane and local anchoring as well as receptor endocytosis and recycling. The molecular mechanisms implicated are numerous and were reviewed recently in great detail. The role of pre-synaptically released neurotransmitters within the complex regulatory apparatus organizing the postsynaptic site underneath presynaptic terminals is not completely understood, even less for inhibitory synapses. In this mini review article, we focus on this aspect of synapse formation, summarizing and contrasting findings on the functional role of the neurotransmitters glycine and γ-aminobutyric acid (GABA) for initiation of postsynaptic receptor clustering and regulation of Cl⁻ channel receptor numbers at inhibitory synapses gathered over the last two decades. Full article

In this study, we propose a bidirectional chemical sensor platform based on a reconfigurable ion-sensitive field-effect transistor (R-ISFET) architecture. The device incorporates Ni-silicide Schottky barrier source/drain (S/D) contacts, enabling ambipolar conduction and bidirectional turn-on behavior for both p-type and n-type configurations. Channel polarity is dynamically controlled via the program gate (PG), while the control gate (CG) suppresses leakage current, enhancing operational stability and energy efficiency. A dual-control-gate (DCG) structure enhances capacitive coupling, enabling sensitivity beyond the Nernst limit without external amplification. The extended-gate (EG) architecture physically separates the transistor and sensing regions, improving durability and long-term reliability. Electrical characteristics were evaluated through transfer and output curves, and carrier transport mechanisms were analyzed using band diagrams. Sensor performance—including sensitivity, hysteresis, and drift—was assessed under various pH conditions and external noise up to 5 V_pp (i.e., peak-to-peak voltage). The n-type configuration exhibited high mobility and fast response, while the p-type configuration demonstrated excellent noise immunity and low drift. Both modes showed consistent sensitivity trends, confirming the feasibility of complementary sensing. These results indicate that the proposed R-ISFET sensor enables selective mode switching for high sensitivity and robust operation, offering strong potential for next-generation biosensing and chemical detection. Full article

AlGaN-based high-electron-mobility transistors are critical for next-generation power electronics and radio-frequency applications, yet achieving stable enhancement-mode operation with a high threshold voltage remains a key challenge. In this work, we designed p-AlGaN superlattices with different structures and performed energy band structure simulations using the device simulation software Silvaco. The results demonstrate that thin barrier structures lead to reduced acceptor incorporation, thereby decreasing the number of ionized acceptors, while facilitating vertical hole transport. Superlattice samples with varying periodic thicknesses were grown via metal-organic chemical vapor deposition, and their crystalline quality and electrical properties were characterized. The findings reveal that although gradient-thickness barriers contribute to enhancing hole concentration, the presence of thick barrier layers restricts hole tunneling and induces stronger scattering, ultimately increasing resistivity. In addition, we simulated the structure of the enhancement-mode HEMT with p-AlGaN as the under-gate material. Analysis of its energy band structure and channel carrier concentration indicates that adopting p-AlGaN superlattices as the under-gate material facilitates achieving a higher threshold voltage in enhancement-mode HEMT devices, which is crucial for improving device reliability and reducing power loss in practical applications such as electric vehicles. Full article

This study presents a photoresist-free patterning method for solution-processed indium zinc oxide (IZO) thin films using two photochemical exposure techniques, namely pulsed ultraviolet (UV) light and UV-ozone, and a plasma-based method using oxygen (O₂) plasma. Pulsed UV light delivers short, high-intensity flashes of light that induce localised photochemical reactions with minimal thermal damage, whereas UV-ozone enables smooth and uniform surface oxidation through continuous low-pressure UV irradiation combined with in situ ozone generation. By contrast, O₂ plasma generates ionised oxygen species via radio frequency (RF) discharge, allowing rapid surface activation, although surface damage may occur because of energetic ion bombardment. All three approaches enabled pattern formation without the use of conventional photolithography or chemical developers, and the UV-ozone method produced the most uniform and clearly defined patterns. The patterned IZO films were applied as active layers in bottom-gate top-contact thin-film transistors, all of which exhibited functional operation, with the UV-ozone-patterned devices exhibiting the most favourable electrical performance. This comparative study demonstrates the potential of photochemical and plasma-assisted approaches as eco-friendly and scalable strategies for next-generation IZO patterning in electronic device applications. Full article

Background: Multiple Chemical Sensitivity (MCS) is a complex, disabling condition marked by non-specific symptoms in response to low-level chemical exposures. It often leads to substantial impairments in quality of life, psychological health, and daily functioning. Although non-pharmacological approaches—such as lifestyle and psychological interventions—are widely used, their clinical effectiveness remains unclear. Objective: We aim to evaluate the effectiveness of lifestyle-based approaches in improving clinical and psychosocial outcomes in adults with Multiple Chemical Sensitivity. Methods: A systematic review was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420251013537). Literature searches were carried out in MEDLINE (PubMed), CINAHL, Google Scholar, and ResearchGate between March and April 2025. Eligible studies included adults (≥18 years) with a confirmed diagnosis of MCS and reported outcomes such as perceived stress, anxiety, depressive symptoms, or quality of life. Methodological quality and risk of bias were independently assessed using the PEDro scale, NIH Quality Assessment Tool, CEBMa checklist, and Cochrane RoB 2.0. Results: Twelve studies (N = 378) met the inclusion criteria. Cognitive and behavioral therapies demonstrated the most consistent evidence of efficacy, with reductions in symptom severity, maladaptive cognitive patterns, and functional limitations. Mindfulness-based stress reduction showed favorable outcomes, while other mindfulness-based interventions yielded mixed results. Exposure-based therapies contributed to increased chemical tolerance and reduced avoidance behavior. Electromagnetic and biomedical approaches demonstrated preliminary but limited effectiveness. Aromatherapy was well tolerated and perceived as relaxing, though its clinical impact was modest. Conclusions: Cognitive and behavioral therapies appear to be most effective among lifestyle-based interventions for MCS/IEI. However, study heterogeneity limits the generalizability of findings, underscoring the need for more rigorous research. Full article

Two-dimensional (2D) materials have been proposed for use in a multitude of applications, with graphene being one of the most well-known 2D materials. Despite their potential to contribute to innovative solutions, the fabrication of such devices still faces significant challenges. One of the key challenges is the fabrication at a wafer-level scale, a fundamental step for allowing reliable and reproducible fabrication of a large volume of devices with predictable properties. Overcoming this barrier will allow further integration with sensors and actuators, as well as enabling the fabrication of complex circuits based on 2D materials. This work presents the fabrication steps for a process that allows the on-wafer fabrication of active and passive radiofrequency (RF) devices enabled by graphene. Two fabrication processes are presented. In the first one, graphene is transferred to a back gate surface using critical point drying to prevent cracks in the graphene. In the second process, graphene is transferred to a flat surface planarized by ion milling, with the gate being buried beneath the graphene. The fabrication employs a damascene-like process, ensuring a flat surface that preserves the graphene lattice. RF transistors, passive RF components, and antennas designed for backscatter applications are fabricated and measured, illustrating the versatility and potential of the proposed method for 2D material-based RF devices. The integration of graphene on devices is also demonstrated in an antenna. This aimed to demonstrate that graphene can also be used as a passive device. Through this device, it is possible to measure different backscatter responses according to the applied graphene gating voltage, demonstrating the possibility of wireless sensor development. With the proposed fabrication processes, a flat graphene with good quality is achieved, leading to the fabrication of RF active devices (graphene transistors) with intrinsic f_T and f_max of 14 GHz and 80 GHz, respectively. Excellent yield and reproducibility are achieved through these methods. Furthermore, since the graphene membranes are grown by Chemical Vapor Deposition (CVD), it is expected that this process can also be applied to other 2D materials. Full article

Accurate and transparent Life Cycle Assessment (LCA) datasets are essential for reliable sustainability evaluations, particularly in the complex and varied textile industry. Historically, the ecoinvent database has been a foundational source for LCA studies in the textile sector. This paper critically examines the limitations of the ecoinvent v3.7 dataset, which is widely used in academic research, industry tools, and policymaking. While newer versions, such as v3.11, released in 2024, have addressed many issues, including enhanced geographical representation and updated emission profiles for chemicals, this study emphasises the historical implications of earlier data versions. By comparing the cradle-to-gate Global Warming Potential (GWP) of wool and polyester jumpers, this research reveals how aggregated and outdated data underestimated the polyester’s environmental impact while overestimating that of wool. These discrepancies have shaped fibre certification, eco-labelling, and consumer perceptions for years. Understanding the legacy of these datasets is vital for re-evaluating past LCA-based decisions and guiding future assessments toward greater regional relevance and transparency. Full article

Polymers characterize a different and important class of materials through various industries, all with unique functional properties and structural attributes. Conventional models of polymer classification depend greatly on labor-intensive methods liable to human error and subjectivity. Hence, a continually growing requirement for new polymers with greater properties is a deep understanding and exploration of the chemical space. Hence, data-driven methods for polymers are developing and able to deal with unique challenges originating from the outstanding physical and chemical range of polymers at smaller and larger scales. Recently, Deep Learning (DL) models have considerably transformed material science by allowing for the automatic study and classification of composite polymers. In this paper, a novel optimization algorithm with a DL-Based Neural Networks for Data-Driven Polymer Classification (OADLNN-DDPC) model is proposed. The main intention of the OADLNN-DDPC model is to improve the classification model for data-driven polymers using state-of-the-art optimization algorithms. The data normalization stage is initially executed via Z-score normalization to convert input data into a beneficial format. In addition, the proposed OADLNN-DDPC model implements the bald eagle search (BES) model for feature selection to detect and retain the most appropriate features. For the polymer classification process, the bidirectional gated recurrent unit (BiGRU) technique is employed. Lastly, the zebra optimizer algorithm (ZOA) is implemented for the tuning process. Extensive experiments conducted on a polymers dataset with 19,500 records and 2048 features demonstrated that OADLNN-DDPC achieves an accuracy of 98.58%, outperforming existing models, such as LSTM (83.37%), PLS-DA (88.18%), and K-NN (98.36%). The simulation process of the OADLNN-DDPC model is performed under the polymer classification dataset. The experimental analysis specified that the OADLNN-DDPC model demonstrated improvement over another existing model. Full article

Saxitoxin (STX) is a potent toxin produced by marine dinoflagellates and freshwater or brackish water cyanobacteria, and is a member of the paralytic shellfish toxins (PSTs). As a highly specific blocker of voltage-gated sodium channels (NaVs), STX blocks sodium ion influx, thereby inhibiting nerve impulse transmission and leading to systemic physiological dysfunctions in the nervous, respiratory, cardiovascular, and digestive systems. Severe exposure can lead to paralysis, respiratory failure, and mortality. STX primarily enters the human body through the consumption of contaminated shellfish, posing a significant public health risk as the causative agent of paralytic shellfish poisoning (PSP). Beyond its acute toxicity, STX exerts cascading impacts on food safety, marine ecosystem integrity, and economic stability, particularly in regions affected by harmful algal blooms (HABs). Moreover, the complex molecular structure of STX—tricyclic skeleton and biguanide group—and its diverse analogs (more than 50 derivatives) have made it the focus of research on natural toxins. In this review, we traced the discovery history, chemical structure, molecular biosynthesis, biological enrichment mechanisms, and toxicological actions of STX. Moreover, we highlighted recent advancements in the potential for detection and treatment strategies of STX. By integrating multidisciplinary insights, this review aims to provide a holistic understanding of STX and to guide future research directions for its prevention, management, and potential applications. Full article

This study explores the provenance of white marble architectural elements from Roman Philippopolis, with a particular focus on the Eastern Gate complex. By determining the origin of the marble, we aim to elucidate economic, social, and urban dynamics related to material selection and trade networks. The investigation examines the symbolic significance of prestigious marble in elite representation and highlights the role of quarry exploitation in the region’s economic and technological development. The Eastern Gate, a monumental ensemble integrated into the city’s urban fabric, was primarily constructed with local Rhodope marble, alongside imported materials such as Prokonnesian marble. Analytical methods included petrographic examination, chemical analysis of trace elements (Mn, Mg, Fe, Sr, Y, V, Cd, La, Ce, Yb, and U), and stable isotope analysis (δ¹⁸O, δ¹³C). Statistical evaluations were performed for each sample (37 in total) and compared with a comprehensive database of ancient quarry sources. The results underscore the dominance of local materials while also indicating selective use of imports, potentially linked to symbolic or functional criteria. The findings support the hypothesis of local workshop activity in the Asenovgrad/Philippopolis area and shed light on regional and long-distance marble trade during the Roman Imperial period, reflecting broader economic and cultural interconnections. Full article

Polymers have a wide range of applications in materials science, chemistry, and biomedical domains. Conventional design methods for polymers are mostly event-oriented, directed by intuition, experience, and abstract insights. Nevertheless, they have been effectively utilized to determine several essential materials; these techniques are facing important challenges owing to the great requirement of original materials and the huge design area of organic polymers and molecules. Enhanced and inverse materials design is the best solution to these challenges. With developments in high-performing calculations, artificial intelligence (AI) (particularly Deep learning (DL) and Machine learning (ML))-aided materials design is developing as a promising tool to show development in various domains of materials science and engineering. Several ML and DL methods are established to perform well for polymer classification and detection presently. In this paper, we design and develop a Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model. This study presents a novel deep learning framework tailored for polymer property classification using SMILES input. By integrating a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) and optimizing the model via Pareto Optimization, the SMILES-PPDCPOA model demonstrates superior classification accuracy and generalization. Unlike existing methods, our model is designed to capture both local substructures and long-range chemical dependencies, offering a scalable and domain-specific solution for polymer informatics. Furthermore, the proposed SMILES-PPDCPOA model executes a one-dimensional convolutional neural network and gated recurrent unit (1DCNN-GRU) technique for the classification process. Finally, the Pareto optimization algorithm (POA) adjusts the hyperparameter values of the 1DCNN-GRU algorithm optimally and results in greater classification performance. Results on a benchmark dataset show that SMILES-PPDCPOA achieves an average classification accuracy of 98.66% (70% Training, 30% Testing) across eight polymer property classes, with high precision and recall metrics. Additionally, it demonstrates superior computational efficiency, completing tasks in 4.97 s, outperforming other established methods such as GCN-LR and ECFP-NN. The experimental validation highlights the potential of SMILES-PPDCPOA in polymer property classification, making it a promising approach for materials science and engineering. The simulation result highlighted the improvement of the SMILES-PPDCPOA system when compared to other existing techniques. Full article

Field-effect transistor (FET) chemical sensors are essential for enabling sophisticated lifestyles and ensuring safe working environments. They can detect a wide range of analytes, including gaseous species (NO₂, NH₃, VOCs), ionic compounds, and biological molecules. Among the structural components of FETs, the gate configuration plays a vital role in controlling the semiconductor channel’s electrostatic environment, thereby strongly influencing sensing performance. Two-dimensional (2D) materials offer additional advantages in these sensors due to their rich surface chemistry and high sensitivity to external interactions. This review offers a comprehensive classification of 2D channel FET chemical sensors based on their gate configurations. Their working principles, fabrication strategies, and sensing performance are discussed in detail. A critical analysis of the advantages and challenges associated with each gate configuration is performed. This review aims to guide future research on the selection of appropriate device configurations for the development of excellent FET chemical sensors. Full article

Carbon fiber is essential in many industries. Since primary production is highly energy-intensive, recycling technologies are being sought. A goal of the research was to develop at a laboratory scale a chemical recycling method aimed at recovering carbon fiber. Two variants of the method have been established and environmentally compared with a primary production version. Methods: The life cycle assessment methodology has been used to assess and quantify the environmental impacts. The cradle to gate analysis was performed with the functional unit defined as a production of 1 kg of carbon fiber. Results: The best environmental option turned out to be a developed chemical recycling technology named Scenario 1. It is a solvolysis performed using an ambient-pressure-operated batch reactor connected to a reflux condenser and an inert gas supply tank, using an ethylene glycol and potassium hydroxide solution. The worst case appeared to be the second variant of the chemical recycling, named Scenario 2 (plasma-enhanced nitric acid solvolysis). Conclusions: In Scenario 1, a production of the ethylene glycol was recognized as a key environmental driver, while in Scenarios 2 and 3 the energy-related impact was the most influential. Full article

Volatile C2–C5 olefins are important bulk chemicals in the polymer industry. Traditionally, C2–C5 olefins are produced from cracked petroleum resources using an energy-consuming and hazardous distillation method. Currently, volatile olefins can be produced from renewable biomass. To obtain polymer-grade volatile olefins from diversified resources, more sustainable and feasible separation techniques need to be developed. This review focuses on two updated separation techniques for C2–C5 olefins: (a) adsorption separation, which separates olefins through porous affinity, the pi complexation effect, and size-exclusion and gate-opening sieving, and (b) liquid absorption separation, which utilizes either organic solvents or ionic liquids for olefin separation. In this review, different separation techniques are compared in terms of their mechanisms and operation conditions in the separation of different types of C2–C5 olefins from variable resources, such as cracked ethylene/propylene/butylene/isoprene and bio-isoprene. Full article