Hasil untuk "Materials Science"

Do Hyeon Kim, Chang Hyeon Ha, Han Been Lee et al.

Abstract In the aftermath of the global COVID-19 pandemic, there is a critical need to develop rapid and sensitive diagnostic devices for point-of-care testing (POCT). Despite the numerous benefits of paper-based colorimetric lateral flow immunoassays (LFAs) in rapid onsite diagnosis, their sensitivity and quantitative analysis capability are limited. To overcome the limitations of the current assays, we developed a new rapid diagnostic method that utilizes glycyrrhizic acid-molybdenum diselenide (GA-MoSe2), a two-dimensional photothermal nanomaterial, for the sensitive detection of C-reactive protein (CRP). GA-MoSe2 was synthesized via a facile liquid exfoliation method using glycyrrhizic acid as a natural surfactant in distilled water. The GA-MoSe2 nanosheet presented a notable photothermal effect, exhibiting an excellent photothermal conversion efficiency of 64.6% and high photothermal stability, and was successfully used as a photothermal sensing probe in an LFA. The GA-MoSe2-based photothermal LFA demonstrated a high analytical performance in CRP detection in the concentration range of 5 to 1000 ng mL− 1, exhibiting a limit of detection of 0.93 ng mL− 1 and up to 7-fold signal enhancement relative to those of traditional gold nanoparticle-based colorimetric LFAs. Moreover, the developed sensor showed high selectivity to CRP even in the presence of interfering substances in serum, excellent reproducibility, and long-term stability over 3 weeks of storage. The GA-MoSe2-based biosensor successfully detected CRP in human serum samples, showing recoveries ranging from 90 to 105% and demonstrating significant capability and feasibility for point-of-care testing. Graphical abstract GA–MoSe₂ nanosheets synthesized via aqueous liquid exfoliation exhibit outstanding photothermal efficiency and stability and were successfully integrated as a photothermal probe into a lateral flow immunoassay for sensitive C-reactive protein detection.

Paul H. Mayrhofer, Helmut Clemens, Franz D. Fischer

Yicheng Tan, Xiuzhen Zhang, Zhichao Liu et al.

This study presents a new waste treatment strategy by dealkalizing red mud with simulated aluminum profile wastewater. We investigate the leaching behavior and the effect of the wastewater dropping rate. During dealkalization, the slurry pH significantly decreases while conductivity increases, indicating efficient dealkalization. The increase in Na⁺ in the filtrate and the decrease in Na₂O in the residue confirm the dissolution of alkali, especially structural alkali. The dealkalization efficiency reaches 64.4%. Increasing the dropping rate accelerates the reaction, enhancing efficiency without altering the process. The mechanism involves Al³⁺ combining with hydrolyzed AlO₂⁻ to precipitate Al(OH)₃, effectively removing structural alkali, which is the main restrictive factor in red mud. After dealkalization, the utilization rate of red mud increases from 5% to 15%, positively impacting economic and environmental aspects, particularly land occupation, soil salinization, and human health.

A. Quadling

This paper represents one half of a set of two, covering the TECHNOLOGY presentations at the Fusion Energy Conference (FEC), held in London, UK, in October 2023. Dominant themes include the use of computing codes to pre-determine machine performance in fusion device design, the impetus that ITER and DEMO are providing for component fabrication innovation, and a growing global focus on blanket materials and fuel retention mechanisms. New themes include power plant costing and the control of activated corrosion products. Stellarator technology is noted, but this is not yet a strong innovation theme at FEC. The latest fusion material science considers operating conditions in power plant service, especially the impact of fuel cycle and superconducting magnet fields; fuel cycle technologies consider electrical coupling and the need for insulating components to control the magnetohydrodynamic response. Optimising correction coils and the thickness of Toroidal Field components to withstand loads is becoming increasing critical in next-step machine design. The latter increasingly utilises integrated frameworks for high-fidelity modelling. Whether coupling armour, blanket and shielding or dose rate fields with geometry, these platforms require high-performance computing. With respect to power plant programmes, the DEMO design continues to assume pulsed operation; JA DEMO, with or without neutral beam injection, sees the electron cyclotron scenario enabling 2 h of pulsed operation at 80 MW of external heating and current drive power. Spherical tokamak work —from the development of the Spherical Tokamak Advanced Reactor in the USA for substantial reduction in weight and cost, to the Spherical Tokamak with Toroidal Magnetic Field 3 T in Mexico, continues to gain momentum. Regulatory approaches are steering away from typical nuclear fission traditions. New safety focus is emerging around transport of activated corrosion products. Outreach on the benefits of fusion must be couched in the framework of legacy, as viewed by a new generation of power stakeholders.

Xiaoling Zuo, Chuan Liu, Kangan Hao et al.

ABSTRACT The development of advanced information storage materials with spatiotemporal security features is critical to address the growing demand for high‐level encryption and anti‐counterfeiting protection. Herein, two types of 4D‐printed fluorescent hydrogels that exhibit time‐gated hierarchical morphing and color‐varying dual functions, driven solely by temperature, have been successfully developed. Specifically, for the first one, the target blooming state of Hydrogels A is realized under 365 nm UV light through synchronized hierarchical morphing and graded fluorescence color transition (orange→blue). For the second one, under 254 nm UV irradiation, doped Hydrogels B exhibit reversible hierarchical state switching between bloomed and closed configurations, accompanied by dynamic multicolor fluorescence modulation. These promising results originate from spatially gradient crosslinking networks precisely engineered via vat photopolymerization (VP) 3D printing, and specially‐designed luminescent chromophores, collectively enabling fluorescent hydrogels to achieve an all‐in‐one stimulus response integrating “shape morphing—multicolor fluorescence—information encryption” under thermal activation. Thus, a unique “codebook”—a time‐dependent dual‐parameter encryption system can be developed using these 4D‐printed fluorescent hydrogels, by dynamically adjusting bending angles and fluorescence ratios, effectively enabling high‐security spatiotemporal information protection. The integration of time‐gated shape‐shifting and multicolor fluorescence enhances encryption complexity through multi‐layered protection. 4D‐printed fluorescent hydrogels enable this bimodal spatiotemporal strategy, preventing unauthorized access while enabling novel secure storage approaches.

Z. Q. Chen, Y. H. Shang, X. D. Liu et al.

Abstract Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

Xiaoqiang Wang, Yakun Tao, Yan Zhou et al.

A comprehensive understanding of cracking mechanisms and the prevention of interfacial microcrack formation are imperative for additive manufacturing of high-performance multi-material heterostructures. This study systematically investigated 316L/CuSn10 heterostructures and identified solidification cracking and solid-state cracking as the predominant mechanisms. Solidification cracking is closely linked to the copper content within the mixing zone, particularly evident at 10% copper content, which heightens sensitivity to solidification cracking due to the widening of intergranular spacing and the elongation of the liquid film channel. Solid-state cracks tend to initiate from pre-existing solidification cracks, propagate along high-angle grain boundaries (HAGBs), particularly within a specific misorientation angle range of 20°–50°, terminating eventually at low-angle grain boundaries (LAGBs). This is mainly controlled by the distribution of dislocations at crack tips, which are dispersed within the grains at LAGBs, and the resulting back stress contributes to crack termination. These findings contribute valuable insights into the cracking mechanisms in heterostructures and offer guidance for the fabrication of crack-free steel-copper components.

Yi Guo, Chaitanya Paramatmuni, Egemen Avcu

Heterophases, such as precipitates, inclusions, second phases, or reinforcement particles, often drive void nucleation due to local incompatibilities in stresses/strains. This results in a significant life-limiting condition, as voids or their coalescence can lead to microcracks that reduce the ductility and fatigue life of engineering components. Continuum-mechanics-based analytical models have historically gained momentum due to their relative ease in predicting failure strain. The momentum of such treatment has far outpaced the development of theories at the atomic and micron scales, resulting in an insufficient understanding of the physical processes of void nucleation and growth. Evidence from the recent developments in void growth theories indicates that the evolution of voids is intrinsically linked to dislocation activity at the void–matrix interface. This physical growth mechanism opens up a new methodology for improving mechanical properties using hydrostatic pressurization. According to the limited literature, with a hydrostatic pressure close to 1 GPa, aluminium matrix composites can be made 70 times more ductile. This significant ductility enhancement arises from the formation of dislocation shells that encapsulate the heterophases and inhibit the void growth and coalescence. With further investigations into the underlying theories and developments of methods for industrial implementations, hydrostatic pressurization has the potential to evolve into an effective new method for improving the ductility and fatigue life of engineering components with further development.

Liheng Bian, Haoze Song, Lintao Peng et al.

Abstract High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique’s state-of-the-art super-resolution SPAD imaging performance.

Xiang Wu, Jiaxuan Zhang, Richu Wang et al.

Cu alloys can be plastically deformed to reach ultra-high strength, but often at an expense of their electrical conductivity. Here we report that the introduction of hierarchical precipitations and the resultant microstructural heterogeneities at different scales could overcome the strength-conductivity tradeoff in Cu-Ag-Zr alloy. The intrinsic particle size dependent precipitation behavior, owing to the different cooling rate during powder atomization, has been inherited after hot isostatic pressing (HIP) of powders into bulk sample. The following cold rolling and aging created multi-scale structures with the sub-micron particles at grain boundaries and sub-micron-to-nano scale precipitates in the grain interior. Those introduced heterogeneous precipitate configurations also altered the evolution of deformation structures during cold rolling and aging, with partially recrystallized grains embedded in highly deformed matrix featured by high density of dislocation and substructures, which results in an excellent combination of tensile strength (704 MPa), electrical conductivity (88.7% IACS), and tensile elongation (14.9%). Besides, no significant coarsening in the micro-nano structures is observed after annealing at 450 °C for 1 h. The findings in this work proposed a novel approach for designing high-strength, high-conductivity, and high-thermal stability copper alloys based on hierarchical precipitation-stimulated structures at nano-to-micron scale.

Svenja Rebecca Sonntag, Stefanie Gniesmer, Anna Gapeeva et al.

In our previous study we were able to show that zinc oxide (ZnO) tetrapods inhibit wound healing processes. Therefore, the aim of this study was to test the antiproliferative effect of two types of porous polydimethylsiloxane (PDMS)/ tetrapodal zinc oxide (ZnO-T) materials, as well as their usability for glaucoma implants. To find the best implant material, two different porous PDMS/ZnO-T materials were examined. One consisted of 3D interconnected PDMS coarse-pored foams with protruding ZnO-T particles; the other consisted of fine-pored 3D interconnected ZnO-T networks homogeneously coated by a thin PDMS film in the nanometer range. Fibroblast cell viability was investigated for both materials via MTT dye, and some implant material samples were further processed for electron microscopy. Both PDMS/ZnO-T materials showed reduced cell viability in the MTT staining. Furthermore, the electron microscopy revealed barely any fibroblasts growing on the implant materials. At the surface of the fine-pored implant material, however, fibroblasts could not be observed in the etched control samples without ZnO-T. It was found that post-processing of the material to the final stent diameter was highly challenging and that the fabrication method, therefore, had to be adapted. In conclusion, we were able to demonstrate the antiproliferative potential of the two different PDMS/ZnO-T materials. Furthermore, smaller pore size (in the range of tens of micrometers) in the implant material seems to be preferable.

Zhen Lu, Chengcai Zhang, Nana Deng et al.

The influence of induction heating on the grain boundary character distribution (GBCD) in near-surface regions of a cold-rolled Nickel-based superalloy was researched. After induction heating, most of low-Σ coincidence site lattice (CSL) boundaries were Σ3 boundaries, which were mainly formed via the growth accident model. Moreover, the grain structures evolution during induction heating had a great influence on the evolution of GBCD. At the low strain of 0.1, both the fraction of Σ3 boundaries and grain size increased with the increasing temperatures, while the former was closely related to the better development of grain-clusters at the higher temperature. In addition, the coherent Σ3 boundaries were easier to be formed at the higher temperature during induction heating, owing to their low interface energy and mobility. At the large strain of 0.5, the fraction of Σ3 boundaries also increased with the increasing temperatures, but the grain size exhibited the opposite trend, which was closely related to the well development of static recrystallization (SRX) behaviors. Meanwhile, there was a “symbiotic relationship” between the SRX grains and Σ3 boundaries during induction heating. Through the electrochemical corrosion tests, it was proved that induction heating can contribute to the improvement of corrosion properties of superalloys via increasing the fraction of Σ3 boundaries, while the best corrosion resistance appeared in the samples treated at 800 °C with the strain of 0.5. Moreover, the evolution of both Σ9 and Σ27 boundaries was also closely related to strains and induction heating temperatures, but their fractions were less than 4%.

Siti Hidayati, Susilawati Susilawati, Ahmad Harjono

This study aimed to develop a valid and feasible problem-based learning tool to improve students' conceptual understanding. This research is a development research using the 4D model. In addition to developing science teaching materials, this research also develops syllabus, lesson plans, and concept understanding instruments. This research was conducted in one of the SMPN Lombok Timur. The validation of learning products was carried out by three validators, media experts and material experts. Expert validation data analysis was carried out using the Pearson validation formula. The results of the feasibility study of integrated student worksheet obtained an average value of 80.6% with very valid criteria. The syllabus got an average score of 77.8%, lesson plans got an average score of 78.2%, and the instrument got an average score of 78%. The average learning tools got an average score of 78.75%. The results of practicality obtained an average practicality of the teacher and student questionnaire responses of 78.75%, while the learning implementation sheet was 77.66%. In conclusion, problem-based learning model learning tools were valid and practical to improve students' conceptual understanding.

Ninghui Liang, Ru Yan, Xinrong Liu et al.

Compressive properties of hybrid polypropylene fiber-reinforced concrete (HPFRC) with different sizes of polypropylene fibers (PPFs) under the impact load (101∼102/s) were tested by using a 74 mm diameter various cross-section split-Hopkinson pressure bar (SHPB), in which the fiber content of fine PPFs was 0.9 kg/m3 and that of coarse PPFs was 6.0 kg/m3. The effect of strain rate and PPF hybridization on the impact characteristics of HPFRC was analyzed. It is found that dynamic compressive properties, including dynamic compressive strength, dynamic compressive strength increase factor (DCF), ultimate strain, and impact toughness, increased with the increase of strain rate. Meanwhile, both fine PPFs and coarse PPFs can enhance the impact strength of concrete, and an appropriate hybridization of two sizes of PPFs in concrete was more effective than the concrete reinforced with one size of PPF. Moreover, a modified constitutive model for HPFRC was proposed based on the Holmquist–Johnson–Cook (HJC) constitutive model. Then, the numerical study of SHPB tests for HPFRC was conducted based on the modified model, which showed that the modified HJC constitutive model could well describe the dynamic stress-strain relationship of HPFRC.

Abrukov Victor, Anufrieva Darya, Lukin Alexander et al.

The results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models of the solid propellants (SP) combustion that solve the direct and inverse tasks are presented. The own analytical platform Loginom was used for the models creation. The models of combustion of double based SP with such nano additives as metals, metal oxides, termites were created by means of experimental data published in scientific literature. The goal function of the models were burning rate (direct tasks) as well as propellants composition (inverse tasks). The basis (script) of a creation of Data Warehouse of SP combustion was developed. The Data Warehouse can be supplemented by new experimental data and metadata in automated mode and serve as a basis for creating generalized combustion models of SP and thus the beginning of work in a new direction of combustion science, which the authors propose to call "Propellant Combustion Genome" (by analogy with a very famous Materials Genome Initiative, USA). "Propellant Combustion Genome" opens wide possibilities for accelerate the advanced propellants development Genome" opens wide possibilities for accelerate the advanced propellants development.