Search Results (70)

Rapid, accurate, and scalable ion sensing technologies are highly desirable for future flexible healthcare and lab-on-a-chip applications. Here, we present a fully roll-to-roll (R2R) gravure-printed single-walled carbon nanotube complementary ring oscillator (SWCNT-cRO)-based microfluidic ion sensing platform fabricated on a flexible substrate. The proposed platform combines scalable printed complementary electronics with frequency-based ion sensing via electrostatically induced top-gating in aqueous microfluidic environments. The fabricated SWCNT-cRO devices exhibited stable oscillation characteristics, with a high device yield (>80%) and continuous manufacturing capability at a web speed of 5.4 m/min. Printable ethanolamine/zirconium acetylacetonate-based n-doping technology enabled complementary SWCNT transistor operation, while multilayer CYTOP/FG-3650 encapsulation ensured stable electrical operation under ionic aqueous conditions. After integration into a polydimethylsiloxane-based microfluidic channel, the oscillation frequency of the SWCNT-cRO was systematically modulated by Na⁺ concentration and pH. The sensing mechanism was based on electrostatically induced carrier modulation in n-type SWCNT transistors, resulting in variations in propagation delay and corresponding shifts in oscillation frequency. Compared with conventional ion-sensitive transistor platforms, the proposed approach offers scalable manufacturing, non-contact ion sensing, elimination of external reference electrodes, and direct compatibility with digital frequency-signal processing systems. This work establishes a promising strategy for future low-cost, disposable, and flexible microfluidic sensing platforms for wearable healthcare and lab-on-a-chip applications, ion sensing, and thin-film transistors. Full article

The increasing demand for reliable and high-performance electric vehicle (EV) batteries requires precise and defect-free welding of battery components. Conventional Gaussian laser beam welding faces challenges such as keyhole instability, spattering, porosity, and brittle intermetallic compound formation, particularly in dissimilar Al-Cu joints. These issues significantly affect the electromechanical performance and durability of battery connections. Beam shaping technology has emerged as a core method for improving weld quality, process stability, and efficiency in laser welding, making laser beam welding increasingly vital for high-volume production of e-mobility components. This review systematically evaluates recent advancements in laser beam shaping for laser welding, especially static beam configurations, such as core-ring profiles, flat top, elliptical, and shaped beams; emphasis has been placed on how altering the intensity distribution influences the challenges associated with conventional welding and emerges as an effective solution to address these challenges. By tailoring the spatial energy distribution, beam shaping improves control of heat input, stabilizes melt pool dynamics, and enhances microstructural uniformity. Static beam shaping, compatible with cost-effective near-infrared continuous-wave laser systems, is already being adopted in industry, whereas dynamic beam shaping remains at an earlier stage of industrial maturity. This review highlights key welding challenges in EV battery manufacturing, evaluates beam shaping strategies as practical solutions, and identifies future research directions for large-scale industrial implementation. Full article

The prevalence of plastics in the environment raises concerns about their complex and poorly understood effects on human health. Research continues to uncover more sources of exposure and wider ranges of plastics within the body. Adverse health effects have been observed in animals, but their relevance to humans remains unclear. To address the growing need for reliable toxicity assessment resources and tools to aid in the synthesis of findings and the identification of data gaps and needs, we have developed a data visualization tool to provide streamlined access to the evaluated data on the chemical impacts of plastics on human health. The Plastics-Related Toxicology Profiles Tool uses Tableau Public to organize the extracted chemical-specific information from ATSDR Toxicological Profiles, the United Nations Environmental Program’s 2023 Chemicals in Plastics Technical Report, and a literature review of relevant research in Google Scholar and PubMed. The tool organizes extracted data from 98 ATSDR Toxicological Profiles representing over 476 substances related to plastics production in 16 tabulated health outcome categories associated with plastics exposure. The chemicals are organized into four categories based on their role in plastics manufacturing. The top four health endpoints impacted by all listed substance profiles are respiratory, neurologic, hepatic, and developmental effects. More than 30% of the substance profiles affected these systems as well as other non-cancer endpoints involving the immunological, renal, and reproductive systems, as well as increased cancer risk in respiratory and hepatic systems. Most monomers negatively impact development and the respiratory system, and most metal additives affect the respiratory system. We explain how this data visualization tool combined with ATSDR’s framework for assessing health impacts from multiple chemicals could be applied to identify the target organs impacted by components of the common plastic polyvinyl chloride. Hazard quotients and index show low toxicity and health risk of components in the cured product. This data provide a valuable resource for prioritizing health risk assessments. Use of this interactive tool can enhance the ability of public health professionals to navigate the expanding literature, synthesize findings, and identify future health risk assessment and research priorities. Full article

Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination. Full article

Greenhouse gas emissions reductions are urgently necessary to mitigate the effects of climate change. Several protocols and agreements are in place to reduce emissions, but global emissions continue to rise nonetheless. This is in part due to emissions offshoring: the shift of manufacturing from countries with developed economies to countries with developing economies. While many countries have achieved reductions through technological advancements, offshoring remains an issue, demonstrated by a global emissions increase despite developed economies reducing their emissions. Trends of atmospheric emission of nitrogen oxides (NO_X), which can be used as a surrogate for gauging sustainability with respect to fossil fuel combustion, confirm this issue. Canada has a developed economy and purports to have reduced emissions in recent decades. Countries accounting for 90% of the dollar value of manufactured goods imported to Canada from 1990 to 2022 were analyzed. Canada shows a decrease in NO_X emissions attributed to manufacturing alongside an increase in imports of manufactured products. Human Development Index (HDI), a United Nations metric for the development level of a country, was plotted against relative manufacturing NO_X emissions on a country basis. There are distinct trends over the time period among low, medium, high, and very high HDI categories for the top countries importing to Canada. Piecewise linear regressions were run for each country, allowing the number of breaks to be equivalent to the number of HDI category changes spanned over the time period. As the HDI category increased, the number of countries with an inverse relationship between HDI and NO_X emissions grew. Countries with very high HDI almost all showed that the HDI increase corresponded to lower emissions of NO_X, while countries with lower HDI categories showed a reduction in this trend. The results support the theory that Canada has offshored manufacturing emissions rather than decreasing the emissions they are responsible for in terms of their manufactured goods. Full article

With Aligned Formable Fibre Technology (AFFT^™), fibers are reformatted into highly oriented epoxy prepreg tapes, enabling the structural reuse of recycled composite waste. The present study investigates whether discontinuous fiber laminates produced with AFFT^™ can be characterized and optimized with the same finite-element workflows long established for continuous fiber composites and whether the resulting structures meet demanding stiffness targets. Initially, various manufacturing methods were adopted, including vacuum bagging, compression molding at 7 bar to simulate autoclave conditions, and compression molding at 90 bar, comprising the three most reasonable manufacturing processes for AFFT^™ laminates. Experimentally measured orthotropic properties were introduced into a finite-element model representing an idealized bicycle top tube, which was chosen as a case study. A genetic algorithm screened candidate stacking sequences, minimizing the combined bending-and-torsion deflection. The best lay-ups reduced deformation by more than 30% compared to a quasi-isotropic baseline, showing that well-oriented short fibers can significantly contribute to the stiffness of composites. Tubes produced with the optimized lay-up were tested in three-point bending tests, and the measured stiffness matched simulations within 5%. These results confirm a key point for sustainable engineering: despite the absence of continuous fibers, conventional simulation strategies accurately predict the performance of AFFT^™ laminates and can be used as the basis for effective genetic optimization. This validation is significant: it enables the design of stiff, high-performance structures from recycled materials using established, cost-effective methods. By proving that optimization strategies developed for traditional continuous fiber composites apply to AFFT^™, this study offers a trusted and accessible pathway to scale circular economy solutions in next-generation composite products. Full article

Powder-bed arc additive manufacturing (PBAAM) may reduce the cost of powder-bed metal additive manufacturing and enable thicker layers than laser powder bed fusion (LPBF), but melt-track stability limits are not well established. Here, 316L stainless steel powder (15–53 µm) was melted by a TIG-based arc in a custom powder-bed system while varying current, travel speed, layer thickness and hatch distance. Single tracks on an inclined bed (≈0–0.4 mm thickness) were used to identify continuity loss and melt-pool width, quantified from top-view images via width profiles, a gap-based continuity metric and the coefficient of variation. Parallel-track tests at 0.15, 0.20 and 0.25 mm layer thickness with hatch distances set to 25%, 50% and 75% of the measured melt-pool width assessed inter-track bonding and lack of fusion, and selected parameters were validated in five-layer builds. Higher current with low-to-moderate travel speeds produced wider, more stable melt pools on the inclined bed. Hatch ratios of 25–50% were the most effective for sustaining fusion in single layers and multi-layer builds, whereas 75% promoted unbonded regions and narrow-track morphologies. Overall, PBAAM can process substantially thicker layers with relatively simple equipment, but requires a narrow, carefully tuned window to balance continuity, fusion and heat accumulation. Full article

This study presents a hybrid experimental–numerical methodology for the preliminary design and optimization of a CFRP crash box intended for high-performance automotive applications. An initial experimental campaign was conducted on frustum-shaped crash boxes manufactured by Pagani Automobili S.p.A., comparing constant and variable thickness configurations through drop tower impact tests to evaluate energy absorption, crushing stability, and failure mechanisms. A lightweight finite element model was developed in Abaqus/Explicit using shell elements and Hashin-based damage criteria, achieving calibration errors below 10% for most parameters and under 15% for peak forces. Geometric enhancements, including continuous flanges, removal of the top surface, and an internal cruciform reinforcement, significantly improved energy absorption (up to 110%) but introduced trade-offs in stroke efficiency and mean force levels. To mitigate these effects, a genetic algorithm was employed to optimize laminate layup by varying ply orientations, resulting in improved stroke efficiency and reduced peak and average forces while maintaining crushing stability. The proposed approach demonstrates that integrating experimental validation with efficient numerical modeling and optimization accelerates the development of lightweight, high-performance crash absorbers, offering a robust framework for motorsport and automotive applications that balances safety, efficiency, and manufacturability. Full article

The continued performance scaling of AI gigafactories requires the development of energy-efficient devices to meet the rapidly growing global demand for AI services. Emerging materials offer promising opportunities to reduce energy consumption in such systems. In this work, we propose an electro-optic microring modulator that exploits a graphene (Gr) and transition-metal dichalcogenide (TMD) interface for phase modulation of data-bit signals. The interface is configured as a capacitor composed of a top Gr layer and a bottom WSe₂ layer, separated by a dielectric Al₂O₃ film. This multilayer stack is integrated onto a silicon (Si) waveguide such that the microring is partially covered, with coverage ratios varying from 10% to 100%. In the design with the lowest power consumption, the device operates at 26.3 GHz and requires an energy of 5.8 fJ/bit under 10% Gr-TMD coverage while occupying an area of only 20 μm². Moreover, a modulation efficiency of V_πL = 0.203 V·cm and an insertion loss of 6.7 dB are reported for the 10% coverage. The Gr-TMD-based microring modulator can be manufactured with standard fabrication techniques. This work introduces a compact microring modulator designed for dense system integration, supporting high-speed, energy-efficient data modulation and positioning it as a promising solution for sustainable AI gigafactories. Full article

This study presents a unified parametric optimization framework for the thermal design of microchannel spreaders used in high-power processor cooling. The fin geometry is expressed in a shape-agnostic parametric form defined by fin thickness, top and bottom gap widths, and channel height, without prescribing a fixed cross-section. This approach accommodates practical fin profiles ranging from rectangular to tapered and V-shaped, allowing continuous geometric optimization within manufacturability and hydraulic limits. A coupled analytical–numerical model integrates conduction through the spreader base, interfacial resistance across the thermal interface material (TIM), and convection within the coolant channels while enforcing a pressure-drop constraint. The optimization uses a deterministic continuation method with smooth sigmoid mappings and penalty functions to maintain constraint satisfaction and stable convergence across the design space. The total thermal resistance ($R_{\mathrm{tot}}$) is minimized over spreader conductivities $k_s=400–2200$ W m⁻¹ K⁻¹ (copper to CVD diamond), inlet fluid velocities $U_{\mathrm{in}}=0.5–5.5$ m s⁻¹, maximum pressure drops of 10–50 kPa, and fluid pass counts $N_p\in \{1,2,3\}$. The resulting maps of optimized fin dimensions as functions of $k_s$ provide continuous design charts that clarify how material conductivity, flow rate, and pass configuration collectively determine the geometry, minimizing total thermal resistance, thereby reducing chip temperature rise for a given heat load. Full article

Employers in South Africa are mandated by labour laws to implement systems of work for the maintenance and promotion of health and safety at work. In response, companies have adopted and implemented occupational health and safety management systems (OHSMSs) whose effectiveness should be continuously monitored through performance measurement. However, there remains no national convention on the specific performance measurement indicators for companies to use. The objective of this study was to determine, characterise and compare lagging indicators adopted and reported by the top 150 Johannesburg Stock Exchange (JSE)-listed companies in South Africa. This qualitative study evaluated annual reports and data books from these companies by analysing textual data through qualitative document analysis. Only 87 of the 150 case companies reported performance using lagging indicators. The basic materials, consumer goods, consumer services and industrial sectors had the most companies which reported performance metrics. Fatality count and lost time injury frequency rate (LTIFR) were the most commonly reported performance metrics and were reported by 64 and 41 companies, respectively. There was variation in the number, type and form of adopted lagging indicators by the case companies. Companies in the manufacturing and mining sectors were more likely to report OHS performance, in general, than those in other sectors. The observed variation across sectors emphasises the need for harmonised indicators to measure and report OHS performance in South Africa. Full article

The COVID-19 pandemic demonstrated both the transformative capacity of vaccine innovation and the persistent inequities that accompany emergency access, underscoring the critical need for stronger collaboration between global health governance and the vaccine industry. Influenza pandemics remain inevitable threats. The continued emergence of avian influenza strains such as H5N1 reinforces the necessity of robust preparedness. This perspective examines the underutilization of private sector vaccine manufacturers in current pandemic influenza frameworks and identifies three central areas where industry participation is indispensable: predictable vaccine demand through robust seasonal influenza programs, economic incentives that de-risk investments in research and development, and diversification of vaccine platforms to expand response capacity. In addition, regionalizing manufacturing, advancing collaborative regulatory models, and negotiating export waivers are presented as potential mechanisms to strengthen equity and supply security. The review highlights demand-based tiered pricing and Advance Purchase Agreements as practical tools to align commercial incentives with public health priorities. Furthermore, it makes the case for embedding private sector representation and knowledge into top-level decision-making and preparedness planning, ensuring investment in innovation is aligned with global health objectives. Ultimately, true pandemic influenza readiness depends on building a sustained seasonal influenza market, embedding private sector engagement into governance structures, and fostering mutual trust to ensure timely access and equitable protection for populations worldwide. Full article

Ammonia fuel is expected to emerge as an effective alternative to fossil fuels due to its zero-carbon nature, high-efficiency storage and transportation advantages, and extensive industrial manufacturing infrastructure. This study discussed the impacts of compression ratio and injection timing on combustion and emission characteristics of an ammonia/diesel dual-fuel (ADDF) engine using numerical simulation. Results indicated that the corresponding optimal indicated thermal efficiency (ITE) continuously increases with an increasing compression ratio. When the compression ratio is 15:1, the injection timing corresponding to the maximum indicated thermal efficiency is −18 °CA after top dead center (ATDC). When the compression ratio ranged from 16:1 to 19:1, the corresponding optimal ITE was achieved at a retarded injection timing of −12 °CA ATDC. At a compression ratio of 19:1, the optimal ITE reached 47.9%. The in-cylinder formation regions of nitrous oxide (N₂O) are closely correlated with NH₃, NO, and temperature distributions, being primarily located at the interface between high-concentration regions of unburned NH₃ and NO. Under the comprehensive impact of increased compression ratio and advanced injection timing, both N₂O and unburned NH₃ emissions show a tendency of increasing first and then decreasing, while NO_x emissions demonstrated a monotonically increasing behavior. Full article

At present, most studies in the field of Wire-Friction Stir Additive Manufacturing (W-FSAM) adopt the side wire feeding method. However, the side wire feeding method has problems in that the wire feeding tube occupies working space and the tool is prone to clogging. To address this, this study proposes a Coaxial Wire Feeding-Friction Stir Additive Manufacturing (CWF-FSAM) method. The CWF-FSAM device adopts a structure where a fixed shaft is coaxially nested inside the stirring shaft, and the fixed shaft is machined with through-channels along the circumferential direction for wire feeding, which eliminates the limitation of the wire feeding tube. This study elaborates on the structure of the CWF-FSAM device, then uses 6061 aluminum alloy as the deposition material for additive manufacturing, and conducts characterization and analysis on the microstructure and mechanical properties of the deposited components. The results show that the interlayer bonding of the deposited components is dense without defects. The components exhibit uniform and fine equiaxed grains, with the average grain sizes of the top, middle, and bottom parts being 3.52 µm, 3.35 µm, and 4.07 µm, respectively. In terms of mechanical properties, the tensile strengths of the components along the building direction (BD) and longitudinal direction (LD) both reach 70% of that of the base material (BM) wire. The hardness ranges from 36 HV to 42 HV. In addition, closed-loop components were prepared by continuous counterclockwise deposition using the CWF-FSAM device. The tensile strengths of the overlapping area, straight section, and corner were 124.45 MPa, 125.88 MPa, and 126.95 MPa, respectively. The overall performance of the closed-loop components is uniform and stable, which indicates that the CWF-FSAM-deposited components have good mechanical property isotropy. Full article

Despite advances in automation, the global manufacturing sector continues to rely heavily on manual package inspection, creating bottlenecks in production and increasing labor demands. Although disruptive technologies such as big data analytics, smart sensors, and machine learning have revolutionized industrial connectivity and strategic decision-making, real-time quality control (QC) on conveyor lines remains predominantly analog. This study proposes an intelligent package integrity monitoring system that integrates waste reduction strategies with both narrow and Generative AI approaches. Narrow AI models were deployed to detect package damage at full line speed, aiming to minimize manual intervention and reduce waste. Using a synthetically generated dataset of 200 paired top-and-side package images, we developed and evaluated 10 distinct detection pipelines combining various algorithms, image enhancements, model architectures, and data processing strategies. Several pipeline variants demonstrated high accuracy, precision, and recall, particularly those utilizing a YOLO v8 segmentation model. Notably, targeted preprocessing increased top-view MobileNetV2 accuracy from chance to 67.5%, advanced feature extractors with full enhancements achieved 77.5%, and a segmentation-based ensemble with feature extraction and binary classification reached 92.5% accuracy. These results underscore the feasibility of deploying AI-driven, real-time QC systems for sustainable and efficient manufacturing operations. Full article