RESUMEN
The effectiveness of the room-temperature strengthening strategy for aluminum (Al) is compromised at increased temperatures due to grain and precipitate phase coarsening. Overcoming the heightened activity of grain boundaries and dislocations poses a significant challenge in enhancing the high-temperature strength through traditional precipitation strengthening. This study presents novel strengthening strategies that integrate intergranular reinforcements, intragranular reinforcements, refined grain, and stacking faults within an (Al2O3+Al3Ti)/Al composite prepared using sol-gel and powder metallurgy technology. Excellent high-temperature tensile properties are achieved; also, a remarkable fatigue performance at increased temperatures that surpasses those of other existing Al alloys and composites is revealed. These superior characteristics can be attributed to its exceptionally stable microstructure and the synergistic strengthening mechanisms mentioned above. This work offers new insights into designing and fabricating thermally stable Al matrix composites for high-temperature applications.
RESUMEN
The fragility of glass describes how rapidly its molecules slow down as it is cooled near its glass transition temperature. In nanoscale films, polymer glasses with higher fragility experience larger reductions in their Tg compared to those with lower fragility. We investigated whether this is due to the free surface of the polymers, which can cause the surface Tg (Tgsurf) to decrease relative to the bulk Tg. By measuring Tgsurf of various polymers, we found that the shift in Tgsurf relative to the bulk Tg increased with fragility. This suggests that more fragile polymers are more susceptible to the free surface effect. We explain this using the concept of energy landscape, as it is used to explain the different slowdown rates between strong and fragile glass formers at Tg.
RESUMEN
The safe service and wide applications of lightweight high-strength aluminum alloys are seriously challenged by diverse environmental corrosion, since high strength and corrosion resistance are mutually exclusive for metals while surface protection cannot provide life-long corrosion resistance. Here, inspired by fish secreting slime from glands to resist external changes, a strategy of incorporating precipitants as the slime into bulk metals using the inner cavity of opened carbon nanotubes (CNTs) as the glands is developed to enable high-strength aluminum alloys with life-long superior corrosion resistance. The resulting material has ultrahigh tensile strength (≈700 MPa) and extraordinary corrosion resistance in acidic, neutral and alkaline media. Notably, it has the highest resistance to intergranular corrosion, exfoliation corrosion and stress-corrosion cracking, compared with all previously reported aluminum alloys, and its corrosion rate is even much lower than that of corrosion-resistant pure aluminum, which results from the pronounced surface enrichment of precipitants released (secreted) from exposed CNTs forming a protective surface film. Such high corrosion resistance is life-long and self-healing due to the on-demand minimal self-supply of the precipitants dispersed throughout the bulk material. This strategy can be readily expanded to other aluminum alloys, and could pave the way for developing corrosion-resistant high-strength metallic materials.
RESUMEN
Machine learning models solute segregation energy based on appropriate features of segregation sites. Lumping many features together can give a decent accuracy but may suffer the curse of dimensionality. Here, we modeled the segregation energy with efficient machine learning using physics-informed features identified based on solid physical understanding. The features outperform the many features used in the literature work and the spectral neighbor analysis potential features by giving the best balance between accuracy and feature dimension, with the extent depending on machine learning algorithms and alloy systems. The excellence is attributed to the strong relevance to segregation energies and the mutual independence ensured by physics. In addition, the physics-informed features contain much less redundant information originating from the energy-only-concerned calculations in equilibrium states. This work showcases the merit of integrating physics in machine learning from the perspective of feature identification other than that of physics-informed machine learning algorithms.
Asunto(s)
Algoritmos , Aprendizaje AutomáticoRESUMEN
Molybdenum disulfide (MoS2) as a typical two-dimensional (2D) transition-metal dichalcogenide exhibits great potential applications for the next-generation nanoelectronics such as photodetectors. However, most MoS2-based photodetectors hold obvious disadvantages including a narrow spectral response in the visible region, poor photoresponsivity, and slow response speed. Here, for the first time, we report the design of a two-dimensional MoS2/GaN van der Waals (vdWs) heterostructure photodetector consisting of few-layer p-type MoS2 and very thin n-type GaN flakes. Thanks to the good crystal quality of the 2D-GaN flake and the built-in electric field in the interface depletion region of the MoS2/GaN p-n junction, photogenerated carriers can be rapidly separated and more excitons are collected by electrodes toward the high photoresponsivity of 328 A/W and a fast response time of 400 ms under the illumination of 532 nm light, which is seven times faster than pristine MoS2 flake. Additionally, the response spectrum of the photodetector is also broadened to the UV region with a high photoresponsivity of 27.1 A/W and a fast response time of 300 ms after integrating with the 2D-GaN flake, exhibiting an advantageous synergetic effect. These excellent performances render MoS2/GaN vdWs heterostructure photodetectors as promising and competitive candidates for next-generation optoelectronic devices.