Roentgenoscopy of laser-induced projectile impact testing.

Laser-induced projectile impact testing (LIPIT) based on synchrotron imaging is proposed and validated. This emerging high-velocity, high-strain microscale dynamic loading technique offers a unique perspective on the strain and energy dissipation behavior of materials subjected to high-speed microscale single-particle impacts. When combined with synchrotron radiation imaging techniques, LIPIT allows for in situ observation of particle infiltration. Two validation experiments were carried out, demonstrating the potential of LIPIT in the roentgenoscopy of the dynamic properties of various materials. With a spatial resolution of 10 µm and a temporal resolution of 33.4 µs, the system was successfully realized at the Beijing Synchrotron Radiation Facility 3W1 beamline. This innovative approach opens up new avenues for studying the dynamic properties of materials in situ.

Negative enthalpy alloys and local chemical ordering: a concept and route leading to synergy of strength and ductility.

Solid solutions are ubiquitous in metals and alloys. Local chemical ordering (LCO) is a fundamental sub-nano/nanoscale process that occurs in many solid solutions and can be used as a microstructure to optimize strength and ductility. However, the formation of LCO has not been fully elucidated, let alone how to provide efficient routes for designing LCO to achieve synergistic effects on both superb strength and ductility. Herein, we propose the formation and control of LCO in negative enthalpy alloys. With engineering negative enthalpy in solid solutions, genetic LCO components are formed in negative enthalpy refractory high-entropy alloys (RHEAs). In contrast to conventional 'trial-and-error' approaches, the control of LCO by using engineering negative enthalpy in RHEAs is instructive and results in superior strength (1160 MPa) and uniform ductility (24.5%) under tension at ambient temperature, which are among the best reported so far. LCO can promote dislocation cross-slip, enhancing the interaction between dislocations and their accumulation at large tensile strains; sustainable strain hardening can thereby be attained to ensure high ductility of the alloy. This work paves the way for new research fields on negative enthalpy solid solutions and alloys for the synergy of strength and ductility as well as new functions.

Negative mixing enthalpy solid solutions deliver high strength and ductility.

Body-centred cubic refractory multi-principal element alloys (MPEAs), with several refractory metal elements as constituents and featuring a yield strength greater than one gigapascal, are promising materials to meet the demands of aggressive structural applications1-6. Their low-to-no tensile ductility at room temperature, however, limits their processability and scaled-up application7-10. Here we present a HfNbTiVAl10 alloy that shows remarkable tensile ductility (roughly 20%) and ultrahigh yield strength (roughly 1,390 megapascals). Notably, these are among the best synergies compared with other related alloys. Such superb synergies derive from the addition of aluminium to the HfNbTiV alloy, resulting in a negative mixing enthalpy solid solution, which promotes strength and favours the formation of hierarchical chemical fluctuations (HCFs). The HCFs span many length scales, ranging from submicrometre to atomic scale, and create a high density of diffusive boundaries that act as effective barriers for dislocation motion. Consequently, versatile dislocation configurations are sequentially stimulated, enabling the alloy to accommodate plastic deformation while fostering substantial interactions that give rise to two unusual strain-hardening rate upturns. Thus, plastic instability is significantly delayed, which expands the plastic regime as ultralarge tensile ductility. This study provides valuable insights into achieving a synergistic combination of ultrahigh strength and large tensile ductility in MPEAs.

The structure of molten calcium ferrite under various redox conditions.

Laser-heated melts based on the 43CaO-57Fe2O3-x eutectic, close to the calcium ferrite (CF) composition, were measured with high-energy X-ray diffraction using aerodynamic levitation over a range of redox states controlled by CO/CO2 gas atmospheres. The iron-oxygen coordination number was found to rise from 4.4 ± 0.3 at 15% Fe3+ to 5.3 ± 0.3 at 87% Fe3+. Empirical potential structure refinement modelling was used to obtain the ferric and ferrous partial pair distribution functions. It was found that the Fe2+ iron-oxygen coordination number is consistently approximately 10% higher in CF than in pure iron oxide, while Fe3+ is essentially identical in all but the most oxygen-rich environments (where it is higher in CF compared with FeOx). The model also shows calcium octahedra to be the dominant species across all redox environments, although the population of CaO7 increases with the availability of oxygen at the expense of CaO4 and CaO5. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 1)'.

Zero-Shot Image Classification Based on a Learnable Deep Metric.

The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples in practice, and some categories even have no training samples at all. The proposed zero-shot learning greatly reduces the dependence on labeled training samples for image classification models. Nevertheless, there are limitations in learning the similarity of visual features and semantic features with a predefined fixed metric (e.g., as Euclidean distance), as well as the problem of semantic gap in the mapping process. To address these problems, a new zero-shot image classification method based on an end-to-end learnable deep metric is proposed in this paper. First, the common space embedding is adopted to map the visual features and semantic features into a common space. Second, an end-to-end learnable deep metric, that is, the relation network is utilized to learn the similarity of visual features and semantic features. Finally, the invisible images are classified, according to the similarity score. Extensive experiments are carried out on four datasets and the results indicate the effectiveness of the proposed method.

Partial activation of α7 nicotinic acetylcholine receptors: insights from molecular dynamics simulations.

Nicotinic acetylcholine receptors (nAChRs) are drug targets for neuronal disorders and diseases. Partial agonists for nAChRs are currently being developed as drugs for the treatment of neurological diseases for their relative safety originated from reduced excessive stimulation. In the current study, molecular docking, molecular dynamics simulations and binding energy calculations were performed to theoretically investigate the interactions between the partial agonists, 4-OH-DMXBA and tropisetron with α7-nAChR. The results suggest that the partial agonists 4-OH-DMXBA and tropisetron bind with α7-nAChR in a binding mode similar to that with AChBP. The non-conserved residues in the binding sites contribute to the orientation deviation of these partial agonists from their orientation in AChBP. Energy calculation and decomposition using MM-GB/SA suggests that the van der Waals term (ΔE(VDW)) is the main driving force for the binding of the partial agonists to α7-nAChR. The molecular dynamics simulations showed that the opening of the C-loop binding with the partial agonists is in-between the openings for the binding with the full agonist and in the apo state. This conformation difference for the C-loop sheds light on the partial agonism of nAChR.