Search Results (3,663)

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

This study investigates a 26th Dynasty Ptah–Sokar–Osiris wooden statuette excavated from the Tari cemetery, Giza Pyramids area, to decode ancient Egyptian manufacturing techniques and establish evidence-based conservation strategies of such wooden objects. Using minimal sampling (1.0–2.0 mm²), integrated XRF, synchrotron-based X-ray diffraction, FTIR, and confocal microscopy distinguished original technological choices from burial-induced alterations. The 85 cm Vachellia nilotica sculpture exhibits moderate structural preservation (cellulose crystallinity index 62.9%) with partial chemical deterioration (carbonyl index 2.22). Complete pigment characterization identified carbon black, Egyptian Blue (cuprorivaite, 55 ± 5 wt %), atacamite-dominated green (65 ± 5 wt %) with residual malachite (10 ± 2 wt %), orpiment (60 ± 5 wt %), red ochre (hematite, 60% ± 5 wt %), white pigments (93 ± 5 wt % calcite), and metallic gold (40 ± 5 wt %). Confocal microscopy revealed sophisticated multi-pigment mixing strategies, with black carbon systematically blended with chromophores for nuanced color effects. Atacamite predominance over malachite provides evidence for chloride-mediated diagenetic transformation over 2600 years of burial. Consistent calcite detection (~ 20–45%) across colored layers confirms systematic ground layer application, establishing technological baseline data for 26th Dynasty Lower Egyptian workshops. Near-complete organic binder loss, severe lignin oxidation, and ongoing salt-mediated mineral transformations indicate urgent conservation needs requiring specialized consolidants, paint layer stabilization, and controlled environmental storage. This investigation demonstrates synchrotron methods’ advantages while establishing a minimally invasive framework for studying polychrome wooden artifacts. Full article

Yeast biosensors represent a promising biotechnological innovation for ensuring the safety and quality of fermented beverages such as beer, wine, and kombucha. These biosensors employ genetically engineered yeast strains to detect specific contaminants, spoilage organisms, or hazardous compounds during fermentation or the final product. By integrating synthetic biology tools, researchers have developed yeast strains that can sense and respond to the presence of heavy metals (e.g., lead or arsenic), mycotoxins, ethanol levels, or unwanted microbial metabolites. When a target compound is detected, the biosensor yeast activates a reporter system, such as fluorescence, color change, or electrical signal, providing a rapid, visible, and cost-effective means of monitoring safety parameters. These biosensors offer several advantages: they can operate in real time, are relatively low-cost compared to conventional chemical analysis methods, and can be integrated directly into the fermentation system. Furthermore, as Saccharomyces cerevisiae is generally recognized as safe (GRAS), its use as a sensing platform aligns well with existing practices in beverage production. Yeast biosensors are being investigated for the early detection of contamination by spoilage microbes, such as Brettanomyces and lactic acid bacteria. These contaminants can alter the flavor profile and shorten the product’s shelf life. By providing timely feedback, these biosensor systems allow producers to intervene early, thereby reducing waste and enhancing consumer safety. In this work, we review the development and application of yeast-based biosensors as potential safeguards in fermented beverage production, with the overarching goal of contributing to the manufacture of safer and higher-quality products. Nevertheless, despite their substantial conceptual promise and encouraging experimental results, yeast biosensors remain confined mainly to laboratory-scale studies. A clear gap persists between their demonstrated potential and widespread industrial implementation, underscoring the need for further research focused on robustness, scalability, and regulatory integration. Full article

Industrial synthetic dyes are among the most common and hazardous pollutants in manufacturing wastewater. In this study, effective dye-decolorizing fungi were isolated from industrial discharge and evaluated for their decolorization efficiency for various dyes, including a triphenylmethane (malachite green, MG), an anthraquinone (reactive blue 19, RB19), and an azo dye (reactive black 5, RB5). The fungus with the highest potential for MG decolorization was identified as Penicillium rubens, whereas Aspergillus fumigatus proved to be the most effective for RB19 and RB5 decolorization. Maximum decolorization for all dyes occurred at pH 9 and 30 °C after 6–7 days of shaking in the dark. Enzyme activity assays revealed that both P. rubens and A. fumigatus produced multiple oxidative and reductive enzymes, including laccase, azoreductase, anthraquinone reductase, triphenylmethane reductase, lignin peroxidase, manganese peroxidase, and tyrosinase. The decolorized filtrates of MG, RB19, and RB5 exhibited very low phytotoxicity for RB5 and no phytotoxicity for MG and RB19. Furthermore, these filtrates demonstrated significant reductions in chemical oxygen demand (46%, 63%, and 50%) and biological oxygen demand (37%, 60%, and 40%) for MG, RB19, and RB5, respectively, compared to untreated dyes. Given their efficient biological removal of dyes under alkaline conditions, these fungal isolates are promising candidates for sustainable wastewater treatment. Full article

Lentiviral transduction remains the gold standard in adoptive modified cellular therapy, such as CAR-T; however, genome integration is not always desirable, such as when treating non-fatal autoimmune disease or for additional editing steps using CRISPR to produce allogeneic CAR-modified cells. Delivering in vitro-transcribed (IVT) mRNA represents an alternative solution but the labile nature of mRNA has led to efforts to improve half-life and translation efficiencies using a range of approaches including chemical and structural modifications. In this study, we explore the role of N⁶–methyladenosine (m6A) in a CD19-CAR sequence when delivered to T cells as an IVT mRNA. In silico analysis predicted the presence of four m6A consensus (DRACH) motifs in the CAR coding sequence and treating T cells with an inhibitor of the m6A methyltransferase (METTL3) resulted in a significant reduction in CAR protein expression. RNA analysis confirmed m6A bases at three of the predicted sites, indicating that the modification occurs independently of nuclear transcription. Synonymous mutation of the DRACH sites reduced the levels of CAR protein from 15 to >50% depending on the T cell donor. We also tested a panel of CAR transcripts with different UTRs, some containing m6A consensus motifs, and identified those which further improved protein expression. Furthermore, we found that the methylation of consensus m6A sites seems to be somewhat sequence-context-dependent. These findings demonstrate the importance of the m6A modification in stabilising and enhancing expression from IVT-derived mRNA and that this occurs within the cell, meaning targeted in vitro chemical modification during mRNA manufacturing may not be necessary. Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

The transition toward Industry 4.0 requires advanced sensing platforms capable of delivering real-time, high-fidelity data under extreme industrial conditions. Thin-film sensors, leveraging both photonic and functional approaches, are emerging as key enablers of this transformation. By exploiting optical phenomena such as Fabry–Pérot interference, guided-mode resonance, plasmonics, and photonic crystal effects, thin-film photonic devices provide highly sensitive, electromagnetic interference-immune, and remotely interrogated solutions for monitoring temperature, strain, and chemical environments. Complementarily, functional thin films including oxide-based chemiresistors, nanoparticle coatings, and flexible electronic skins extend sensing capabilities to diverse industrial contexts, from hazardous gas detection to structural health monitoring. This review surveys the fundamental optical principles, material platforms, and deposition strategies that underpin thin-film sensors, emphasizing advances in nanostructured oxides, 2D materials, hybrid perovskites, and additive manufacturing methods. Application-focused sections highlight their deployment in temperature and stress monitoring, chemical leakage detection, and industrial safety. Integration into Internet of Things (IoT) networks, cyber-physical systems, and photonic integrated circuits is examined, alongside challenges related to durability, reproducibility, and packaging. Future directions point to AI-driven signal processing, flexible and printable architectures, and autonomous self-calibration. Together, these developments position thin-film sensors as foundational technologies for intelligent, resilient, and adaptive manufacturing in Industry 4.0. Full article

CO₂ laser processing has emerged as an efficient dry-finishing technique capable of inducing controlled chemical and morphological transformations in cotton and denim textiles. The strong mid-infrared absorption of cellulose enables localised photothermal heating, leading to selective dye decomposition, surface oxidation, and micro-scale ablation while largely preserving the bulk fabric structure. These laser-driven mechanisms modify colour, surface chemistry, and topography in a predictable, parameter-dependent manner. Low-fluence conditions predominantly produce uniform fading through fragmentation and oxidation of indigo dye; in comparison, moderate thermal loads promote the formation of carbonyl and carboxyl groups that increase surface energy and enhance wettability. Higher fluence regimes generate micro-textured regions with increased roughness and anchoring capacity, enabling improved adhesion of dyes, coatings, and nanoparticles. Compared with conventional wet processes, CO₂ laser treatment eliminates chemical effluents, strongly reduces water consumption and supports digitally controlled, Industry 4.0-compatible manufacturing workflows. Despite its advantages, challenges remain in standardising processing parameters, quantifying oxidation depth, modelling thermal behaviour, and assessing the long-term stability of functionalised surfaces under real usage conditions. In this review, we consolidate current knowledge on the mechanistic pathways, processing windows, and functional potential of CO₂ laser-modified cotton substrates. By integrating findings from recent studies and identifying critical research gaps, the review supports the development of predictable, scalable, and sustainable laser-based cotton textile processing technologies. Full article

Cancer immunotherapy increasingly relies on nucleic acid-based vaccines, yet achieving efficient and safe delivery remains a critical limitation. Polyplexes—electrostatic complexes of cationic polymers and nucleic acids—have emerged as versatile carriers offering greater chemical tunability and multivalent targeting capacity compared to lipid nanoparticles, with lower immunogenicity than viral vectors. This review summarizes key design principles governing polyplex performance, including polymer chemistry, architecture, and assembly route—emphasizing microfluidic fabrication for improved size control and reproducibility. Mechanistically, effective systems support stepwise delivery: tumor targeting, cellular uptake, endosomal escape (via proton-sponge, membrane fusion, or photochemical disruption), and compartment-specific cargo release. We discuss therapeutic applications spanning plasmid DNA, siRNA, miRNA, mRNA, and CRISPR-based editing, highlighting preclinical data across multiple tumor types and early clinical evidence of on-target knockdown in human cancers. Particular attention is given to physiological barriers and engineering strategies—including size-switching systems, charge-reversal polymers, and tumor-penetrating peptides—that improve intratumoral distribution. However, significant challenges persist, including cationic toxicity, protein corona formation, manufacturing variability, and limited clinical responses to date. Current evidence supports polyplexes as a modular platform complementary to lipid nanoparticles in selected oncology indications, though realizing this potential requires continued optimization alongside rigorous translational development. Full article

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems. Full article

New nano-biomaterials for specific dentistry applications have been developed thanks to contributions from materials science. The present work aims to characterize the physicochemical properties of a composite nanomaterial scaffold in block form for maxillofacial bone regeneration applications. The scaffold was composed of block-shaped elements and consisted of a mixture of nano-hydroxyapatite, β-tricalcium phosphate, and type I collagen of bovine origin. Collagen I molecule is biodegradable, biocompatible, easily available, and a natural bone matrix component. The biomaterial was analyzed using a range of methods, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), chemical composition microanalysis, and X-Ray diffractometry (XRD). The wettability was measured. This was carried out by measuring the contact angle of a 0.9% NaCl solution on the surface. Differential scanning calorimetry (DSC) was used to measure the phase transformation temperatures. In the SEM and TEM analyses, it was possible to identify the layers of the materials and, with microanalysis, quantify their chemical composition. The XRD spectra showed the presence of nano-hydroxyapatite and ß-TCP. Wettability testing revealed that the material is highly hydrophilic, and BM-MSC culture analyses demonstrated that the biomaterial can promotes cell adhesion and interaction. The higher wettability is due to the higher density of the porous material observed in the SEM analysis. The results of the DSC testing showed that the sample analyzed undergoes endothermic transitions and transformation between 25 and 150 °C. The first phase transformation during heating occurs at 61.1 °C, which is above body temperature. The findings demonstrated that the composite was devoid of any contamination arising from manufacturing processes. It can be concluded that the n-HA/β-TCP and type 1 collagen are free of manufacturing contaminants. They also have high wettability, which increases the spreading of body fluids on the biomaterial’s surface and its interactions with cells and proteins. This makes them suitable for clinical application. Full article

A total of 250 known and novel compounds—ketamine and serotonergic psychedelics or their analogues—designed to target depression, addictions and/or other mental or neurological disorders and developed as “recreational” (illegal) drugs from three chemical families, ergolines, tryptamines and phenylethylamines, were investigated in the context of their ability to cross the human placenta. Using a novel multivariate model involving compounds’ drug-likeness (according to Lipinski’s Ro5), caco-2 membrane permeability, fraction unbound to plasma proteins, steady-state volume of distribution and the total count of heteroatoms (non-carbon atoms with hydrogens included), it was established that the majority of studied compounds are likely to cross the placenta easily, most probably by the passive diffusion mechanism. Atomic contributions of structural elements of studied compounds were investigated using the Morgan fingerprinting algorithm and it was postulated that the fragments promoting transport of compounds across the placenta are carbonyl, hydroxyl, nitro- and phosphoryloxy groups—rigid polycyclic structures, bulky alkyl/aryl groups and halogen atoms restrict the trans-placental passage. All studied compounds are expected to be relatively easily obtained by synthetic routes, which makes them an interesting target for manufacturers of illegal drugs and warrants the need to pursue pharmacological studies of these compounds in silico. Full article

Metformin, a 60-year-old biguanide and cornerstone of type 2 diabetes therapy, continues to challenge and inspire modern pharmaceutical science. Despite its chemical simplicity, metformin displays highly complex pharmacokinetic and pharmacodynamic behavior driven by transporter dependence, luminal activity, and formulation-sensitive exposure. Originally regarded as limited by low permeability and incomplete absorption, metformin has emerged as a paradigm for gut-targeted therapy, controlled- and delayed-release systems, and personalized pharmaceutics. Growing evidence has repositioned the intestine, rather than systemic plasma exposure, as a major site of action, highlighting the central role of organic cation transporters and multidrug efflux systems in determining efficacy, variability, and gastrointestinal tolerability. Beyond metabolic control, insights into transporter regulation, pharmacogenetics, microbiome interactions, and manufacturing quality have expanded metformin’s relevance as a model compound for contemporary drug development. Advances in formulation design, quality-by-design manufacturing, and regulatory control have further reinforced its clinical robustness, while repurposing efforts in oncology, immunometabolism, and regenerative medicine underscore its translational potential. This review integrates mechanistic pharmacology, formulation science, and clinical translation to position metformin not merely as an antidiabetic agent, but as a didactic model illustrating the evolution of pharmaceutics from molecule-centered design to system-oriented, precision-driven therapy. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Background/Objectives: The clinical application of CAD/CAM restorative materials continues to evolve due to increasing demand for aesthetic, durable, and minimally invasive indirect restorations. Hybrid nanoceramics, such as Grandio disc (VOCO GmbH, Cuxhaven, Germany), are increasingly used in indirect restorative dentistry due to their favourable combination of mechanical strength, polishability, wear resistance, and bonding potential. One challenge associated with adhesive protocols for CAD/CAM materials lies in achieving durable bonds with resin cements. Extensive post-polymerization during fabrication reduces the number of unreacted monomers available for chemical interaction, thereby limiting the effectiveness of traditional adhesive strategies and necessitating specific surface conditioning approaches. This study aimed to evaluate, in a preliminary, non-inferential manner, the influence of several combined conditioning protocols on surface micromorphology, elemental composition, and descriptive SBS trends of a CAD/CAM hybrid nanoceramic. This work was designed as a preliminary pilot feasibility study. Due to the limited number of specimens (two discs per protocol, each providing two independent enamel bonding measurements), all bond strength outcomes were interpreted descriptively, without inferential statistical testing. This in vitro study investigated the effects of various surface conditioning protocols on the adhesive performance of CAD/CAM hybrid nanoceramics (Grandio disc, VOCO GmbH, Cuxhaven, Germany) to dental enamel. Hydrofluoric acid (HF) etching was performed to improve adhesion to indirect resin-based materials using two commercially available gels: 9.5% Porcelain Etchant (Bisco, Inc., Schaumburg, IL, USA) and 4.5% IPS Ceramic Etching Gel (Ivoclar Vivadent, Schaan, Liechtenstein), in combination with airborne-particle abrasion (APA), silanization, and universal adhesive application. HF may selectively dissolve the inorganic phase, while APA increases surface texture and micromechanical retention. However, existing literature reports inconsistent results regarding the optimal conditioning method for hybrid composites and nanoceramics, and the relationship between micromorphology, elemental surface changes, and adhesion remains insufficiently clarified. Methods: A total of ten composite specimens were subjected to five conditioning protocols combining airborne-particle abrasion with varying hydrofluoric acid (HF) concentrations and etching times. Bonding was performed using a dual-cure resin cement (BiFix QM) and evaluated by shear bond strength (SBS) testing. Surface morphology was examined through environmental scanning electron microscopy (ESEM), and elemental composition was analyzed via energy-dispersive X-ray spectroscopy (EDS). Results: indicated that dual treatment with HF and sandblasting showed descriptively higher SBS, with values ranging from 5.01 to 6.14 MPa, compared to 1.85 MPa in the sandblasting-only group. ESEM revealed that higher HF concentrations (10%) created more porous and irregular surfaces, while EDS indicated an increased fluorine presence trend and silicon reduction, indicating deeper chemical activation. However, extending HF exposure beyond 20 s did not further improve bonding, suggesting the importance of protocol optimization. Conclusions: The preliminary observations suggest a synergistic effect of mechanical and chemical conditioning on hybrid ceramic adhesion, but values should be interpreted qualitatively due to the pilot nature of the study. Manufacturer-recommended air abrasion alone may provide limited adhesion under high-stress conditions, although this requires confirmation in studies with larger sample sizes and ageing simulations. Future studies should address long-term durability and extend the comparison to other hybrid CAD/CAM materials and to other etching protocols. Full article