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Despite the widespread applications of high-pressure plasma in semiconductor and display industry, such as deposition and ashing process, the use of cutoff probes for diagnosing high-pressure plasma was rarely studied. In this study, we investigated a method of measuring electron density in a cutoff probe using the resonance peak in a high-pressure plasma environment. This method is validated through both electromagnetic wave simulations and experimental methodologies. Our findings reveal that the proposed method demonstrates discrepancies of less than 1.47% compared to the input plasma frequency in the results of electromagnetic wave simulations at a gas pressure of 10 mTorr, while at 2.5 Torr, it exhibited a maximum discrepancy of 13.3% when selecting resonance frequencies lower than the electron–neutral collision frequency. This discrepancy at high pressure is reduced to within 1.92% by selecting a resonance frequency higher than the electron–neutral collision frequency. Also, the feasibility of these electron density measurements has been confirmed under conditions of high gas pressure where the cutoff frequency is not measurable, as evidenced by both simulation and experimental results. Our research on the diagnostic methods in high-pressure plasmas could significantly enhance the measurement and interpretation of plasma parameters in various industrial processes. [ABSTRACT FROM AUTHOR]

Time-resolved photoemission electron microscopy (PEEM) has established itself as a versatile experimental technique to unravel the ultrafast electron dynamics of materials with nanometer-scale resolution. However, the approach of performing PEEM-based, pixel-by-pixel lifetime mapping has not been reported thus far. Herein, we describe in detail the data pre-processing procedure and an algorithm to perform time-trace fittings of each pixel. We impose an energy cutoff for each pixel prior to spectral integration to enhance the robustness of our approach. With the energy cutoff, the energy-integrated time traces show improved statistics and lower fitting errors, thus resulting in a more accurate determination of the fit parameters, e.g., decay time constants. Our work allows us to reliably construct PEEM-based lifetime maps, which potentially shed light on the effects of local microenvironment on the ultrafast processes of the material and allow spatial distributions of lifetimes to be correlated with observables obtained from complementary microscopic techniques, hence enabling a more comprehensive characterization of the material. [ABSTRACT FROM AUTHOR]

Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

The microstructure plays a crucial role in the manufacturing and application of polyacrylonitrile fibers, which serve as precursors for carbon fibers. Synchrotron radiation small angle x-ray scattering (SR-SAXS) is a non-destructive and precise technique for analyzing fiber structures. This study employed one-dimensional SR-SAXS mapping to extract key structural parameters such as periodicity, lamellae thickness, and the extent of amorphous regions, as well as the directional orientation in γ-irradiated, pre-oxidized polyacrylonitrile fibers. The analysis revealed a three-layered structure comprising a surface skin, a transitional layer, and a central core. Notably, the lamellar thickness exhibits a "U"-shaped distribution, while the long-period structures, amorphous regions, and orientational properties demonstrate a "wave-like" pattern. Within this structure, the skin exhibits a higher level of orientation, with the orientation decreasing progressively from the skin toward the core layer. The structure of the layered crystal was further corroborated by the morphological analysis. In addition, molecular simulations were performed to propose the mechanisms underlying the formation of this layered structure. This comprehensive investigation using SR-SAXS and one-dimensional mapping provides detailed insights into the microstructural and morphological characteristics of polyacrylonitrile fibers, which can inform future advancements in material processing and refinement techniques for the production of advanced fibers. [ABSTRACT FROM AUTHOR]

We present a novel method for generating optical vortex needle beams (focused optical vortices with extended depth-of-focus) using a compact flat multilevel diffractive lens (MDL). Our experiments demonstrate that the MDL can produce focused optical vortices (FOVs) with topological charges l = 1 − 4 (extendable to other l values), maintaining focus over distances significantly longer than conventional optical vortices. Specifically, FOVs exhibit non-diffracting behavior with a depth-of-focus (DOF) extended beyond 5 cm, compared to conventional optical vortices, which show continuous size increase due to diffraction. When the MDL is illuminated by an optical vortex of 3 mm diameter, it achieves a transmission efficiency of approximately 90 % and extends the DOF several times beyond that of traditional lenses. Increasing the size of the input optical vortex further extends the DOF but introduces additional rings, with their number increasing proportionally to the value of l. Our approach, validated by both experimental results and numerical simulations, proves effective for beams such as optical vortex and Hermite-Gaussian modes and holds potential applications in high-resolution imaging, material processing, optical coherence tomography, and three-dimensional optical tweezers, offering a simple and efficient solution for generating non-diffracting beams. [ABSTRACT FROM AUTHOR]

In approaches to analyze material composition or in processing tasks using ultra-short laser ablation, it is of particular interest how ablated materials are distributed across the solid angle in front of the interaction region. We found that with our sub-10-fs laser in the regime from 10 14 W / c m 2 to 10 17 W / c m 2 , the solid angle of the ablation cone decreases significantly along with the laser intensity in a vacuum environment. For this observation, we used ion-beam analysis to investigate the distribution of tungsten collected on silicon catcher plates arranged across the solid angle of the laser-ablation cone. Moreover, we used other post-mortem tools, such as scanning electron microscopy and confocal laser scanning microscopy, to determine the ablation threshold of F t h = 468 mJ / c m 2 of our tungsten samples. Here, clearly, two laser intensity-dependent ablation regimes can be observed in the detection of a crater depth and a ablation cone angle. [ABSTRACT FROM AUTHOR]

We study the impedance behavior of two ferrofluids, of a similar magnetic material, one constituted by spherical nanoparticles and the other constituted by cubes, both suspended in kerosene. The ferrofluid constituted by cubic nanoparticles has 10% doping of a rare earth ion. The samples were inserted between two parallel disk-like electrodes of area S = 2.3 c m 2 made of surgical steel, separated by d = 127 μ m. The impedance was measured by applying a sinusoidal voltage of amplitude V 0 = 30 mV, from 1 mHz to 100 kHz. To analyze the experimental data, we use a model based on the Poisson–Nernst–Planck equations, with Ohmic boundary conditions. In the analysis, we assume that the ferrofluids contain free ions, originated from the manufacturing process, released by the stabilization layer around the magnetic nanoparticles dispersed in kerosene. The corresponding nanoparticles are charged of opposite signs with respect to these free ions. In the high frequency region, the effective diffusion coefficient coincides with that from the free diffusion coefficients, defined as the mathematical average between the diffusion coefficients of the nanoparticles and the free ions. In the low frequency region, we found the ambipolar diffusion coefficient, defined as their harmonic average. The effect of the electrodes is taken into account by means of surface conductivity to describe the conduction current across the electrode, assumed to be proportional to the surface electric field. In this model, the role of the electrodes is important just in the low frequency region. On the contrary, in the high frequency region, where the electric current is dominated by the displacement current, the role of the electrodes is negligible. The results show that the nanoparticles of the magnetic material have no effects on the higher-frequency range of the impedance spectra. In the low frequency region, our results indicate a difference in the electric response of the two ferrofluids. Due to their similar dimensions and, hence, similar ambipolar diffusion coefficients, we impute the observed different behavior to the charge transfer from the bulk to the external circuit included in the surface conductivity. [ABSTRACT FROM AUTHOR]

The freezing/melting transition is at the heart of many natural and industrial processes. In the classical picture, the transition proceeds via the nucleation of the new phase, which has to overcome a barrier associated with the free energy cost of the growing nucleus. The total nucleation rate is also influenced by a kinetic factor, which somehow depends on the number of attempts to create a nucleus, that translates into a significant density of proto-nuclei in the system. These transient tiny nuclei are not accessible to experiments, but they can be observed in molecular simulations, and their number and size distributions can be acquired and analyzed. The number distributions are carefully characterized as a function of the system size, showing the expected behavior, with limited spurious effects due to the finite simulation box. It is also shown that the proto-nuclei do exist even in the stable phase, in agreement with the fact that the (unfavorable) volume contribution to their free energy is negligible in the first stages of nucleation. Moreover, the number and size distributions evolve continuously between the stable and the metastable phases, in particular when crossing the coexistence temperature. The size distributions associated with any nucleus and with the largest one have also been calculated, and their relationship recently established for bubbles in a liquid [Puibasset, J. Chem. Phys. 157, 191102 (2022)] has been shown to apply here. This is an important relation for free energy barrier calculations with biased molecular simulations. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are promising candidate materials for photo-driven processes. Their crystalline and tunable structure makes them well-suited for placing photoactive molecules at controlled distances and orientations that support processes such as light harvesting and photocatalysis. In order to optimize their performance, it is important to understand how these molecules evolve shortly after photoexcitation. Here, we use resonance Raman intensity analysis (RRIA) to quantify the excited state nuclear distortions of four modified UiO-68 MOFs. We find that stretching vibrations localized on the central ring within the terphenyl linker are most distorted upon interaction with light. We use a combined computational and experimental approach to create a picture of the early excited state structure of the MOFs upon photoactivation. Overall, we show that RRIA is an effective method to probe the excited state structure of photoactive MOFs and can guide the synthesis and optimization of photoactive designs. [ABSTRACT FROM AUTHOR]

Manganese–rhodium (Mn–Rh) nanoparticles have emerged as a promising candidate for catalytic applications in the production of syngas, a critical precursor for a wide range of industrial processes. This study employs a comprehensive, theoretical, and computational approach to investigate the structural and electronic properties of Mn–Rh nanoparticles, with a specific focus on their interaction with titanium oxide (TiO2) surfaces and their potential as catalysts for syngas reactions. The density functional theory calculations are employed to explore the adsorption behavior of Mn–Rh nanoparticles on TiO2 surfaces. By analyzing the adsorption energies, geometries, and electronic structure at the nanoscale interface, we provide valuable insights into the stability and reactivity of Mn–Rh nanoparticles when immobilized on TiO2 supports. Furthermore, the catalytic performance of Mn–Rh nanoparticles in syngas production is thoroughly examined. Through detailed reaction mechanism studies and kinetic analysis, we elucidate the role of Mn and Rh in promoting syngas generation via carbon dioxide reforming and partial oxidation reactions. The findings demonstrate the potential of Mn–Rh nanoparticles as efficient catalysts for these crucial syngas reactions. This research work not only enhances our understanding of the fundamental properties of Mn–Rh nanoparticles but also highlights their application as catalysts for sustainable and industrially significant syngas production. [ABSTRACT FROM AUTHOR]

A novel bilateral impact-ionization-avalanche-transit-time (BIMPATT) diode based on AlGaN/GaN two-dimensional electron gas is proposed in this article. The BIMPATT is compatible with the available GaN high electron mobility transistor (HEMT) manufacturing process and has a shorter actual electron transit distance than existing HEMT-like IMPATT (HIMPATT) diodes. Compared with the same-sized HIMPATT, the optimum frequency of BIMPATT rises from 320 to 420 GHz and possesses a far wider operating frequency band, especially in the near 0.9 THz range. The maximum DC-RF conversion efficiency rises from 12.9% to 17.6%. The maximum RF power of BIMPATT is 3.18 W/mm, which is similar to 3.12 W/mm of the HIMPATT. Furthermore, our simulation demonstrated that the characteristics of BIMPATT are significantly affected by the length of anode and the thickness of the AlGaN barrier layer. The effects of ohmic contact resistance and background impurities on BIMPATT are also taken into account. This paper provides a reference for the design and characteristics enhancement of the lateral IMPATT devices. [ABSTRACT FROM AUTHOR]

The nucleation of ice from aqueous solutions is a process essential to myriad environmental and industrial processes, but the physical factors affecting the capacity of different solutes to depress the homogeneous nucleation temperature of ice are yet poorly understood. In this work, we demonstrate that for many binary aqueous solutions of non-ionic solutes, this depression is dominated by the entropy of the liquid phase. Employing the classic Turnbull interpretation of the interfacial free energy γ ∼ T S l i q u i d − S s o l i d and estimating solution entropies with a Flory-style modification of the ideal entropy of mixing that accounts for solute size effects, we demonstrate that mixing entropy alone predicts experimental homogeneous nucleation temperatures across a wide variety of non-ionic solutions. We anticipate that this physical insight will not only enhance a fundamental understanding of homogeneous nucleation processes across fields but also open new avenues to the rational design of aqueous solutions for desired nucleation behaviors. [ABSTRACT FROM AUTHOR]

The technological landscape for industrial processes handling asphaltene is evolving at a rapid pace due to the increase in the extraction of heavy crude oil. The main underlying challenges in this regard are the flow assurance, the recovery of the spent solvent, and the sophisticated extractor setup required to develop the process to an industrial scale. The number of studies focused on the handling of the asphaltene at the atomic and molecular scales is growing enormously in order to identify new sustainable solvents for the effective extraction of asphaltene from heavy crude oil or oil-bearing sands. This Perspective focuses on the importance of density functional theory and molecular dynamics simulations to explore the broader range of asphaltene inhibitors, e.g., nanoparticles, ionic liquids, and deep eutectic solvents, to prevent asphaltene precipitation. We provide a concise overview of the major accomplishments, analyze the aspects that require attention, and highlight the path-breaking studies having a significant impact on the process of chemical enhanced oil recovery from heavy crude oil reservoirs primarily based on atomistic and molecular simulations. [ABSTRACT FROM AUTHOR]

Polymer blends are critical in many commercial products and industrial processes and their phase behavior is therefore of paramount importance. In most circumstances, such blends are formulated with samples of high dispersity, which have generally only been studied at the mean-field level. Here, we extend the renormalized one-loop theory of concentration fluctuations to account for blends of disperse polymers. Analyzing the short and long length-scale fluctuations in a consistent manner, various measures of polymer molecular weight and dispersity arise naturally in the free energy. Thermodynamic analysis in terms of moments of the molecular weight distribution(s) provides exact results for the inverse susceptibility and demonstrates that the theory is not formally renormalizable. However, physically motivated approximations allow for an "effective" renormalization, yielding (1) an effective interaction parameter, χe, which depends directly on the sample dispersities (i.e., Mw/Mn) and leaves the form of the mean-field spinodal unchanged, and (2) an apparent interaction parameter χa that depends on higher-order dispersity indices, for instance Mz/Mw, and characterizes the true limits of blend stability accounting for long-range off-critical fluctuations. We demonstrate the importance of dispersity on several example systems, including both "toy" models that may be realized in computer simulation and more realistic industrially relevant blends. We find that the effects of long-range fluctuations are particularly prominent in blends where the component dispersities are mismatched, especially when there is a small quantity of the high-dispersity species. This can be understood as a consequence of the shift in the critical concentration(s) from the monodisperse value(s). [ABSTRACT FROM AUTHOR]

SnO2 is a prototypical transparent conducting oxide that finds widespread applications as transparent electrodes, gas sensors, and transparent thin-film devices. Hydrogen impurities in SnO2 give rise to unintentional n-type behavior and unexpected changes to conductivity. Interstitial H (Hi) and H at an oxygen vacancy (HO) are both shallow donors in SnO2. An O–H vibrational line at 3155 cm−1, that can be produced by a thermal anneal at 500 °C followed by a rapid quench, has been assigned to the Hi center and is unstable at room temperature on a timescale of weeks. An IR absorption study of the decay kinetics of the 3155 cm−1 O–H line has been performed. The disappearance of Hi upon annealing has been found to follow second-order kinetics. Measurements of the decay rate for a range of temperatures have determined an activation energy for the diffusion of interstitial H in SnO2. These results provide fundamental information about how unintentional hydrogen impurities and their reactions can change the conductivity of SnO2 device materials in processes as simple as thermal annealing in an inert ambient. [ABSTRACT FROM AUTHOR]

The study of granular avalanches in rotating drums is not only essential to understanding various complex behaviors of interest in granular media from a scientific perspective; it also has valuable applications in regard to industrial processes and geological catastrophes. Despite decades of research studies on avalanches, a proper understanding of their dynamic properties still remains a great challenge to scientists due to a lack of state-of-the-art techniques. In this study, we accurately predict the avalanche dynamic features of three-dimensional granular materials in rotating drums, by using graph neural networks on the basis of their initial static microstructures alone. We find that our method is robust to changes in various model parameters, such as the interaction potential, size polydispersity, and noise in particle coordinates. In addition, with the grain-scale velocities obtained either from our network or from numerical simulations, we find an approximately equal and strong correlation between the global velocity and global velocity fluctuation in our 3D granular avalanche systems, which further demonstrates the predictive power of our trained graph neural networks to uncover the fundamental physics of granular avalanches. We expect our method to provide more insight into the avalanche dynamics of granular materials and other amorphous systems in the future. [ABSTRACT FROM AUTHOR]

Solid-state reactions proceeding from an initial microstructure enable the synthesis of complex materials in a wide variety of applications. However, despite the importance of such reactions in materials' processing, the connection between an initial, reactant microstructure and the ensuing transformation kinetics has been relatively little studied. In this work, we employ computer simulation of a reaction–diffusion model and a quantitative analysis of the associated kinetic equations to examine the propagation and interaction of evolving fronts in a prototypical system transforming via a solid-state reaction. It is found that the interaction between fronts dictates transformation kinetics. We then use our results to describe the kinetics of phase formation in the Co–Ti–O system, which contains the entropy-stabilized line compound cobalt dititanate, CoTi 2 O 5. This system is of particular interest as it has been shown that the solid-state synthesis may be exploited to produce single-crystal cobalt dititanate. [ABSTRACT FROM AUTHOR]

In material processing and handling processes, premixed interlayer often replace the ideal Ni/Al interface, which would become a new origin of alloying reaction. This work investigates shock-induced reaction mechanism and kinetics of premixed Ni/Al nanolaminates with molecular dynamics simulations and theoretical analysis. The reaction is found to be driven by the crystallization evolution in premixed interlayer and the diffusion of premixed atoms. Among them, multi-stage reaction patterns are strongly manifested by the crystallization evolution characteristics. Specifically, "crystallization-dissolution-secondary growth" and "crystallization-dissolution" of B2 phase respectively correspond to the solid-state and solid-liquid reaction cases, where crystallizations are fitted well by Johnson–Mehl–Avrami kinetics model. Interestingly, the different growth mechanisms of B2 grain are revealed, namely nuclei coalescence and atomic diffusion. Moreover, the analysis of microscopic diffusion theory indicates a certain non-random diffusion nature for solid-state reaction initiation, but near-purely random diffusion for solid-liquid reaction initiation. The diffused Al atoms possess a limited diffusion coefficient and enhanced diffusion correlation, resulting in extremely slow mixing rate in Ni layer. In addition, the influence law of Ni concentration in premixed interlayer on reactivity parameters can be quantitatively described by a quadratic function. [ABSTRACT FROM AUTHOR]

The air fabrication of bulk LREBa2Cu3O7−δ [where LRE can be Nd, Sm, Eu, Gd, or combinations such as (Y,Gd), (Y,Er), (Sm,Eu,Gd), (Nd,Eu,Gd), and (Nd,Sm,Gd)] holds the potential to revolutionize the cost-effective production of high-temperature superconductors. This method enables batch processing on an industrial scale, opening up a range of applications across various fields with enhanced superconducting performance. In this study, we fabricated large single-domain (Sm,Eu,Gd)Ba2Cu3O7−δ using the top-seeded infiltration growth process in air. To suppress the RE/Ba substitutions, BaO2 was added in the secondary (Sm,Eu,Gd)2BaCuO5 (SEG-211) phase with various BaO2 weight percentages. The optimized sample achieved an onset critical temperature Tc(onset) of 94.57 K and a transition width of ΔTc < 1 K, representing a 71% improvement in ΔTc over the reference sample. Furthermore, the self-field critical current density Jc of the BaO2-optimized sample was 33.55 kA/cm2 at 77 K and 62.26 kA/cm2 at 65 K, H//c-axis. The addition of Ag2O to the BaO2-optimized sample yielded an onset critical temperature of Tc(onset) > 93 K and a transition width of ΔTc < 1.5 K. Significant improvement in high-field critical current density was observed in the Ag2O-added sample, with a Jc value of 18.14 kA/cm2 at 77 K and 1 T, and 50.54 kA/cm2 at 65 K and 3 T, H//c-axis. These results indicate that ternary (Sm,Eu,Gd)Ba2Cu3O7−δ can be produced in air using the IG process with optimal BaO2 and Ag2O, which is crucial for batch production in air. [ABSTRACT FROM AUTHOR]

[Display omitted] Lipidic mesophase drug carriers have demonstrated the capacity to host and effectively deliver a wide range of active pharmaceutical ingredients, yet they have not been as extensively commercialized as other lipid-based products, such as liposomal delivery systems. Indeed, scientists are primarily focused on investigating the physics of these systems, especially in biological environments. Meanwhile, the production methods remain less advanced, and researchers are still uncertain about how the manufacturing process might affect the quality of formulations. Bringing these products to the market will require an industrial translation process. In this scenario, we have developed a robust strategy to produce lipidic mesophase-based drug delivery systems using a dual-syringe setup. We identified four critical process parameters in the newly developed method (dual-syringe method), in comparison to eight in the standard production method (gold standard), and we defined their optimal limits following a Quality by Design approach. The robustness and versatility of the proposed method were assessed experimentally by incorporating drugs with diverse physicochemical properties and augmented by machine learning which, by predicting the drug release from lipidic mesophases, reduces the formulation development time and costs. [ABSTRACT FROM AUTHOR]

Variable sampling plans incorporating process capability indices (PCIs) have been introduced to adapt to the changing landscape of modern manufacturing, where the occurrence of defective goods has significantly decreased. Skip-lot sampling plans (SkSP) are particularly effective for batches characterized by consistent and high product quality. Additionally, manufacturers with a strong track record of producing high-quality goods or possessing empirical knowledge about their production processes can benefit from resampling. It has been demonstrated that resubmitted sampling plans outperform traditional single sampling plans. In this study, a novel variant of skip-lot sampling, referred to as SkSP-SR, is proposed. SkSP-SR utilizes two reference plans: one for normal inspection (utilizing SSP) and the other for skip inspection (utilizing RSP). The approach also considers the loss of information associated with deviations from the target by incorporating the Taguchi capability index. Compared to traditional sampling plans, the suggested approach not only reduces sample sizes but also enhances discriminating power. Comprehensive tables presenting plan parameters for commonly used quality and risk criteria are provided, enabling practitioners to readily implement the recommended plan. [ABSTRACT FROM AUTHOR]

BACKGROUND: Enshi Yulu tea (ESYL) is the most representative of steamed green tea in China, but its aroma formation in processing is unclear. Thus, the ESYL volatiles during the whole industrial processing were investigated using headspace solid‐phase microextraction coupled with gas chromatography–mass spectrometry. RESULTS: A total of 134 volatiles were identified. Among these, 31 differential volatiles [P < 0.05 and variable importance in projection (VIP) > 1] and 25 key volatiles [relative odor activity value (rOAV) and/or the ratio of each rOAV to the maximum rOAV (ROAV) > 1.0] were screened out, wherein β‐ionone and nonanal were the most key odorants. Besides, the sensory evaluation combined with multivariate statistical analysis of volatiles pinpointed spreading, fixation, first drying, and second drying as the key processing steps that have a pronounced influence on the aroma quality of ESYL. Furthermore, the oxidative degradation of unsaturated fatty acids, synthesis of monoterpenes, and degradation of carotenoids were the main metabolic pathway for the formation of key odorants. CONCLUSION: The study provides comprehensive insights into the volatile characteristics during the industrial processing of ESYL and promote our understanding of the aroma formation in steamed green teas. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Natural fibers are lightweight, cost‐effective, readily available, and eco‐friendly materials. However, natural fiber‐reinforced composites are constrained by biological inconsistency and inferior fiber‐matrix interfacial properties, which restrict effective processing using advanced additive manufacturing methods. In this study, we develop a novel concept for natural fiber composite interfaces by growing hydroxyapatite (HAP) nanocrystals on jute fibers. The resulting hybrid composite is characterized using mechanical testing, nanoindentation, and modulus mapping. The interfacial region suggests a 31.4% increase in stiffness and possesses a storage modulus of up to 7.5 GPa. The tensile modulus and strength of the hybrid composite improves by 30% and 33%, respectively. Furthermore, we develop a novel process for the additive manufacturing of jute fiber thermoset composites through a direct writing (DW) process. The HAP modification increases thermal conductivity, consequently improving the curing process during DW and enhancing composite manufacturability. We demonstrate that the DW process enables the printing of intricate multilayer geometries with varying fiber content. Highlights: Hydroxyapatite is used for natural fiber composite interfacial modification.Nanoindentation shows the interfacial region exhibits 31.4% higher stiffness.Modified composites possess superior mechanical performance.Jute fibers are thermally functionalized for composite additive manufacturing.A direct writing method is developed for continuous functionalized jute fiber. [ABSTRACT FROM AUTHOR]

To explore the potential application of plant fiber reinforced composites for automotive component applications, this study prepared bamboo fiber (BF)/nano‐talc/polypropylene (PP) composites based on the injection molding process, comprehensively evaluated the effect of reinforcement materials on the forming properties of composites, including thermal performance, mechanical properties, water absorption, etc. Furthermore, taking a certain automotive injection molded interior part as the object, a life‐cycle assessment from production to the gate was conducted based on the real energy and material consumption during the composite preparation process. The results indicate that adding BF and talc powder increased the thermal stability, density, hardness, viscosity, and crystallinity of the composites while reducing the water contact angle on the surface. Surface‐modified BF and PP showed good compatibility. Talc powder exhibited good dispersibility in PP, and the synergistic effect of BF and talc powder effectively enhanced the composite performance. The tensile, flexural, and impact strength of the composites were improved by 40.64%, 51.48%, and 66.51%, respectively, compared with pure PP. The modulus of the composite increased nearly 2 times compared with pure PP. Additionally, the composite demonstrated good friction and wear properties. The environmental impact of the BF composite manufacturing process was significantly higher than that of pure PP. The substantial consumption of electricity, chemicals, and water resources in the extraction and modification processes of BF were the main factors. The findings of this study contribute to achieving green, high‐performance BF composite manufacturing and the expansion of its applications. Highlights: Composites have a higher environmental impact compared to PP.BF and talc can synergistically enhance the performance of composites.BF shows good compatibility with PP.Composites exhibit good friction and wear characteristics. [ABSTRACT FROM AUTHOR]

Applications of high‐performance plastics and composites have widely been expanded to various industries due to their superior properties, such as high strength‐to‐weight ratio, chemical resistance, and thermal/electrical insulation. However, the numerous possible combinations of polymers and reinforcements/fillers, the variability of these materials, and their complex manufacturing processes pose challenges in terms of efficiently developing new plastics and composites, accurately modeling their properties, and effectively monitoring and controlling their manufacturing processes. Integrating data‐driven techniques, such as machine learning, artificial intelligence, and big data analytics, is a promising pathway to overcome these challenges as it is demonstrated by the state‐of‐the‐art research works presented in this special issue. This article provides background to the readers and introduces the range of topics covered by the articles in this special issue. [ABSTRACT FROM AUTHOR]

Carbon Fiber Reinforced Polymers (CFRP) is one of the ideal materials in the field of automotive lightweight. As one of the traditional manufacturing processes of CFRP, autoclave process has long production time, high cost and complicated operation. To overcome these shortcomings, deep drawing can be used for CFRP forming. It has the advantages of stable product quality, good repeatability, low cost, simple operation and can produce parts with complex shapes. In this paper, the influence of lay‐up sequences on the drawability of cylindrical parts was studied by using self‐made deep drawing device. The results showed that among the four lay‐up sequences [0]°12, [0/90]°6, [0/90/45/‐45/90/0]°S and [15/30/....../180]°, the [15/30/....../180]° lay‐up parts had excellent deformation coordination ability due to less angular variation in the fiber direction, and the maximum depth of 21.99 mm could be obtained. Due to the influence of wrinkles, the thickness of [15/30/....../180]° parts increased at the die corner and side wall. The thickness decreased at the flange, punch corner and bottom due to the compressive pressure. In addition, [15/30/....../180]° lay‐up parts benefited from the high level of in‐plane isotropy of the helicoidal structure, it had the fewest forming defects. The resin poor areas and resin rich areas were less and no fiber cracks were found. Highlights: Investigated the influence of lay‐up sequences on the drawability of CFRP.The forming depth of [15/30/....../180]° parts was 21.99 mm.[15/30/....../180]° lay‐up parts had the best drawability.No cracks were found on the [15/30/....../180]° part. [ABSTRACT FROM AUTHOR]

Ionizable lipids are widely recognized as the crucial component of lipid nanoparticles (LNPs). They enable mRNA encapsulation, shield it from enzymatic degradation, facilitate cellular uptake, and foster its cytosolic release for subsequent translation into proteins. In addition, PEGylated lipids are added to stabilize the particles in storage and in vivo. In this study, we investigate the potency of LNPs prepared using commonly adopted ionizable and pegylated lipids in vitro (using HEK293 cells) and in vivo (mouse studies) to consider the impact of structure on potency. LNPs were prepared using a fixed molar ratio of DSPC: Cholesterol: ionizable/cationic lipid: PEG lipid (10:38.5:50:1.5 mol%). All LNP formulations exhibited similar critical quality attributes (CQAs), including particle size <100 nm, low PDI (<0.2), near-neutral zeta potential, and high encapsulation efficiency (>90%). However, the potency of these LNPs, as measured by in vitro mRNA expression and in vivo expression following intramuscular injection in mice varied significantly. LNPs formulated with SM-102 exhibited the highest expression in vitro , whilst in vivo SM-102 and ALC-0315 LNPs showed significantly higher mRNA expression than DLin-MC3-DMA (MC3), DODAP and DOTAP LNPs. We also investigated the effect of PEG lipid choice (ALC-0159, DMG-PEG2k, and DSPE-PEG2k), which did not impact LNP CQAs, nor their clearance from the injection site. However, PEG lipid choice significantly influenced mRNA expression with the incorporation of DSPE-PEG2k reducing expression. This work contributes valuable insights to the evolving landscape of mRNA research, emphasizing that CQAs are a marker of the quality of the LNP production process, but not discriminatory regarding LNP potency. Similarly, standard in vitro studies do not provide insights into in vivo potency. These results further emphasize the intricacies of formulation design and the importance of bridging gaps between experimental outcomes in different settings. [Display omitted] [ABSTRACT FROM AUTHOR]

Reactive carbon capture (RCC) processes involve the capture of carbon dioxide (CO2) and conversion to a value-added product using a single sorbent/reaction medium. Not only can RCC processes generate valuable byproducts that can reduce the cost of carbon capture, but RCC tends to have lower energy demand than processes involving the transfer of CO2 between the mediums used for capture and subsequent reactions. Saline water has been proposed as a potential medium for RCC due to it's relative abundance and low cost. Additionally, the composition and chemistry of many saline water sources: (1) elevates the CO2 content (as compared to atmospheric concentrations), (2) provides various cations that can form valuable products with CO2, and (3) enhances the kinetics of chemical reactions used to convert CO2 to stable byproducts. In addition to established industrial processes for converting CO2 into inert or valuable byproducts, we found 20 new processes and technologies that have been developed specifically to capture and convert CO2 using saline water. Both preexisting and emerging processes can be broadly classified as electrochemical or chemical titration processes. When assessing the potential viability of applying any of these processes for large scale carbon capture, several factors must be considered, such as the net carbon footprint of the process, the market size, location of customers and value of the end product, the energy demand and chemical costs of the process, and any other environmental impacts. The feasability of many emerging saline-based RCC processes is difficult to determine, as many technologies were tested using synthetic saline waters and/or concentrated CO2 sources. Notwithstanding the early stage of development of many saline-based RCC technologies, the major limitation to implementation of this approach to carbon capture is the mismatch in the scale of the markets for products of saline-based RCC and the scale of carbon capture needed to meet climate goals. However, because the products of many of the processes reviewed here are stable and non-hazardous, these technologies may also be used for carbon sequestration efforts where the products are managed as waste, in which case the carbon capture potential of these technologies can surpass the market-imposed limitations on RCC. Thus, the potential benefits of saline water-based RCC identified in this review encourage further study and development of these technologies. [ABSTRACT FROM AUTHOR]

The utilization of metal Additive Manufacturing (AM) has led to substantial progress in the manufacturing process of metal components. Evaluating the influence of the wide variety of factors related to the material type, AM process, and the resultant microstructure and properties is not easy and accurate by traditional engineering strategies. Therefore, one of the most effective ways to improve AM performance is to employ artificial intelligence methods, such as Machine Learning (ML), to establish complex links and enhance control over systems and product quality. The integration of these powerful ML techniques presents an emerging opportunity to revolutionize manufacturing processes, tackle production challenges, and optimize resource consumption. Consequently, in this review, the role of machine learning in laser-based metal additive manufacturing is explored by highlighting its applications in process optimization and property prediction. Evaluation of the results reported in the literature indicates that it is possible to establish relationships between process, structure, and properties by considering inputs such as part geometry, material properties, microstructural characteristics, and AM process parameters and utilizing ML algorithms. Defect detection and in-situ monitoring are among the other applications of ML algorithms in AM procedures, enhancing the manufacturability and repeatability of metal components. For this purpose, various linkages and correlations for Directed Energy Deposition and Laser Powder Bed Fusion are outlined in this review. The advancement in hardware and software will boost the advantage of applying data-driven approaches to overcome the obstacles in metal AM. [ABSTRACT FROM AUTHOR]

Ejection is the most critical phase in the injection moulding process and depends on several variables that can affect and difficult the demoulding process. Some phenomena in the mould, such as shrinkage, adhesion and friction, can lead to higher ejection forces. Friction, deformation and adhesion mechanisms are components of the coefficient of friction that occurs between the mould and the part surface. An increase in the coefficient of friction will result in higher ejection forces and consequently increase the energy consumption during the demoulding process. Mould and part material, mould surface roughness, coatings and ejection temperature are some of the variables that can affect the coefficient of friction in the injection moulding process. This review paper presents some considerations on the mechanisms, the process and material variables that can affect the coefficient of friction. [ABSTRACT FROM AUTHOR]

Selective laser melting has become among the most popular laser powder bed fusion processes in additive manufacturing technologies in the past decades, thanks to the variety of printed materials available in the market, and to the mechanical and physical properties outcomes. Nevertheless, a special consideration should be given to the surface quality which not only influences the mechanical, dynamic behavior but also the electrochemical resistance of the as-built non-finished selective laser melted parts. Therefore, this work is fully dedicated to the understanding of the as-built surface texture according to four process parameters, namely building-orientation (Oth), laser power (P), scan speed (V), and hatch spacing (h); 3D topography and 2D profiles were captured resulting in substantial surface sampling for the statistical analysis; further optical microscopy followed trying to draw the interplay between the balls formation and the standardized roughness indices. The arithmetic mean roughness (Ra) ranged between 7.55 and 37.8 µm, while the best surface quality was reached for top face of the horizontal samples, power at 200 W, scan speed at 1500 mm/s, and the lowest hatch spacing of 100 µm. Moreover, it was found that the Rp statistics seems to be more adapted to the analysis of the roughness of the as-built surfaces compared to the other roughness indices. The ANOVA findings revealed that the building and faces' orientations can be assumed as the most significant for all responses, while laser power, scan speeds, and hatch space are found to highly influence the balls dimensions. The balls' dimensions also contribute to the smoothness or roughness of the as-built samples drawing thus an indirect relationship between P, V, and h on the roughness level. [ABSTRACT FROM AUTHOR]

AbstractThe mechanical properties of large-tow carbon fibers (LCFs) reinforced polymers are crucial for determining practical value and future prospects. This article investigates the mechanical properties and influencing factors of LCF by conducting longitudinal tensile and flexural tests on laminates made from LCFs. The experimental results revealed that the tensile performance of the laminates was 46% lower than the predictions from finite element analysis (FEA). The maximum destructive stress loss is nearly doubled. Even worse, the flexural performance has been compromised by about 3.8 times. To understand the reasons behind this discrepancy, scanning electron microscopy (SEM) revealed significant progressive damage on the fracture surfaces of the laminates. This damage resulted from the infiltration of resin and fibers during the manufacturing process. Additionally, the experimental load-displacement curve indicates that this localized damage occurs prior to the overall fracture failure, which greatly diminishes the overall performance of the laminates. The research findings suggest that, despite using relatively precise LCF laminate manufacturing methods, the inherent properties of the fibers limit the enhancement of their mechanical properties. This study offers valuable insights for improving the manufacturing processes of LCFs laminates and for enhancing the mechanical performance. [ABSTRACT FROM AUTHOR]

Milling chatter, a form of self-excited vibration, can cause significant damage in machining and manufacturing processes. By selecting appropriate milling parameters, milling chatter can be effectively mitigated without sacrificing milling efficiency. Within the framework of the semi-discretization scheme, this paper introduces the Newton–Simpson-based predictor–corrector methods to compute milling stability lobe diagrams. Firstly, the milling delay differential equation is transformed into the state space form, and then the time-delayed term and the periodic coefficient matrix of the state space equation are treated as an operator. Secondly, the tooth passing period is divided into the free vibration period and the forced vibration period. During the forced vibration period, the time-delayed term and the periodic coefficient matrix are approximated as a holistic operator over two different time intervals using the Newton interpolation polynomials and the Simpson formula, respectively. Finally, the state transition matrix is constructed based on the predictor–corrector scheme, and the stability lobe diagrams are obtained by applying Floquet theory. The convergence rate and calculation accuracy of the proposed methods are compared with those of the existing predictor–corrector methods, semi-discretization, and full-discretization methods. The results show that the proposed Newton–Simpson-based predictor–corrector methods have a faster convergence rate. For the local stability lobe diagrams, the arithmetic mean of relative error (AMRE), mean squared error (MSE), and the sum of absolute error (SAE) of the proposed methods are in the ranges of 0.003 to 0.004, 2.66 × 10–10 to 6.40 × 10–10, and 6.41 × 10–4 to 9.34 × 10–4, respectively, which are much lower than those of the existing methods, indicating that the proposed methods have higher calculation accuracy than the existing methods. The current work has a broad application prospect in the field of milling stability prediction for precision machining and the selection of chatter-free milling parameters. [ABSTRACT FROM AUTHOR]

The electrochemical oxidation of amines has emerged as a promising alternative to traditional energy‐intensive industrial processes for the green synthesis of nitriles, and the rational design of efficient electrocatalysts is crucial due to the sluggish reaction kinetics. Herein, it is reported that modulating the topological order within the same structural unit can boost catalytic activity for the electrooxidation of benzylamine to benzonitrile (BOR). Using nickel nitride as a model, Cu atoms are introduced into the pristine N─Ni structural framework, inducing a change in the topological order of the structural units and resulting in the formation of Ni3CuN with an antiperovskite structure. As a result, the antiperovskite Ni3CuN exhibits a 2‐fold enhancement of BOR activity compared to Ni3N, despite the fact that the N─Ni coordination polyhedra of both are almost identical. Theoretical calculations demonstrate that the modulation strategy of the topological order of the same structural units successfully weakens the Ni─N bonding interactions, induces the downward shift of the d‐band centers, and optimizes the desorption of the reaction products, which ultimately contributes to the more superior electrocatalytic activity of Ni3CuN. This work provides a new perspective on developing advanced electrocatalysts through the structural topological order modulation. [ABSTRACT FROM AUTHOR]

The gradual separation of the naphtha steam cracking product (propane/propylene/ethylene, C3H8/C3H6/C2H4) to obtain highly pure C3H6 is of great significance as it provides an alternative process for this important industrial feedstock. However, developing adsorbents to meet the efficient separation and purification application remains challenging. A water coordination‐decorated metal–organic framework (MOF, Cd‐dtzip‐H2O) material is reported. Interestingly, water molecules cooperate with the framework to build a water vortex and significantly affect the preferential adsorption for C3H6. At 298 K and 100 kPa, the loading of C3H6 in the material is 112.0 cm3 g−1, equivalent to 1.7 and 2.0 times of C3H8 and C2H4, respectively. Experimental breakthroughs show that the material efficiently separates C3H6/C3H8 and C3H6/C2H4 under different pressures. Meanwhile, it also unprecedentedly achieves the one‐step separation of C2H4/C3H8/C3H6 mixtures yielding high‐purity C3H6 (99.9%) and C2H4 (99.95%) products with the productivities of 38.2 and 12.7 L kg−1, respectively. [ABSTRACT FROM AUTHOR]

The fabrication of soft fluid‐filled systems with mm or sub‐mm scale features such as tuneable lenses, microfluidic tactile sensors, and electrohydraulic zipping actuators often relies on manual filling. This final step of injecting fluid limits throughput and repeatability. This study presents an additive manufacturing process to create complex stretchable systems in which the liquid is directly printed as part of the fabrication process. The devices actuate straight out of the printing setup, with no further filling or sealing steps. In our fully printed approach for multi‐layered structures, the key steps are the deposition of precisely shaped liquid droplets with sub‐µL resolution, followed by their encapsulation using a freezing process to allow printing or spraying over the temporarily solid fluid drops. Printing both the fluid and the structure has advantages of: a) no filing channel, enabling very high fill‐factors; b) design freedom as each device in array can be filled with a different volume; c) accuracy in filling. We demonstrate this process by printing arrays of 5 mm diameter stretchable hydraulically amplified taxels (HAXELs), consisting of nine flexible layers, with the working fluid included as part of the printing process. [ABSTRACT FROM AUTHOR]