Search

In this paper, the influence of rare earth (RE) on the microstructure and mechanical properties of austenitic high manganese steel (HMnS) Mn15Cr2V were investigated. The results showed that the microstructure, hardness and impact strength of RE modification sample is finer and better than non-modified sample. Under the effect of impact load, the hardness and the depth of the work-hardening layer of the modified steel was higher than that of the non-modified steel, thereby, the value of microhardness in the surface of the modified sample was 420 HV while it was only 395 HV in the non-modified sample. The value of the impact strength of the modified sample was up to 132 J/cm2 compared to the non-modified sample is only 115 J/cm2. Moreover, after impact load, the austenite nanoparticles had been found out on the surface of this steel, this is the cause of the increasing of mechanical properties in this steel.

Hydrogen-evolution reaction (HER) is a promising technology for renewable energy conversion and storage. Electrochemical HER can provide a cost-effective method for the clean production of hydrogen. In this study, a biomimetic eco-friendly approach to fabricate nitrogen-doped carbon nanosheets, exhibiting a high HER performance, and using a carbonized polydopamine (C-PDA), is described. As a biopolymer, polydopamine (PDA) exhibits high biocompatibility and can be easily obtained by an environmentally benign green synthesis with dopamine. Inspired by the polymerization of dopamine, we have devised the facile synthesis of nitrogen-doped nanocarbons using a carbonized polydopamine for the HER in acidic media. The N-doped nanocarbons exhibit excellent performance for H2 generation. The required overpotential at 5 mA/cm2 is 130 mV, and the Tafel slope is 45 mV/decade. Experimental characterizations confirm that the excellent performance of the N-doped nanocarbons can be attributed to the multisite nitrogen doping, while theoretical computations indicate the promotion effect of tertiary/aromatic nitrogen doping in enhancing the spin density of the doped samples and consequently in forming highly electroactive sites for HER applications.

Abstract Deep learning (DL) models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties. To address these limitations, we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships. The proposed architecture is evaluated using two well-known datasets (the QM9 and the Materials Project datasets), and three in-house-developed computational materials datasets. Train–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities, which are comparable to those of current state-of-the-art models. Furthermore, comparative validations, based on first-principles calculations, indicate that the degree of attention of the atoms’ local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties. These properties encompass molecular orbital energies and the formation energies of crystals. The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

Conducting polymers (CPs) have been widely studied to realize advanced technologies in various areas such as chemical and biosensors, catalysts, photovoltaic cells, batteries, supercapacitors, and others. In particular, hybridization of CPs with inorganic species has allowed the production of promising functional materials with improved performance in various applications. Consequently, many important studies on CPs have been carried out over the last decade, and numerous researchers remain attracted to CPs from a technological perspective. In this review, we provide a theoretical classification of fabrication techniques and a brief summary of the most recent developments in synthesis methods. We evaluate the efficacy and benefits of these methods for the preparation of pure CP nanomaterials and nanohybrids, presenting the newest trends from around the world with 205 references, most of which are from the last three years. Furthermore, we also evaluate the effects of various factors on the structures and properties of CP nanomaterials, citing a large variety of publications.

New Nd–Fe–B crystal structures can be formed via the elemental substitution of LA–T–X host structures, including lanthanides (LA), transition metals (T) and light elements, X = B, C, N and O. The 5967 samples of ternary LA–T–X materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe and all light-element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data-driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure–stability relationship of the newly created Nd–Fe–B crystal structures. For predicting the stability for the newly created Nd–Fe–B structures, three supervised learning models: kernel ridge regression, logistic classification and decision tree model, are learned from the LA–T–X host crystal structures; the models achieved maximum accuracy and recall scores of 70.4 and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieved an accuracy and recall score of 72.9 and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure–stability relationship of the Nd–Fe–B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd–Fe–B crystal structures.

A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.

Poly(3,4-ethylenedioxy thiophene):poly(styrenesulfonate) (PEDOT:PSS) has been investigated as a hole transport material in organic-inorganic hybrid perovskite solar cells (PSCs) with a p-i-n architecture. However, the intrinsic acidic nature of PEDOT:PSS induces interfacial protonation and degrades the quality of the thin film, resulting in low charge conductivity, low perovskite crystallinity, and poor device stability. In this work, we demonstrate the effect of adding carbon quantum dots doped with silver (Ag-CQDs) to the PEDOT:PSS hole transport layer (HTL) to improve charge extraction, increase electrical conductivity, and hence, enhance the performance of hybrid PSCs. The proposed method enables facile one-step hydrothermal synthesis of the Ag-CQDs in the presence of a nitrogen-containing carbon precursor. The incorporation of PEDOT:PSS containing Ag-CQDs in PSCs noticeably increased the short circuit current density (Jsc) in the optimised devices, enhancing the device performance and ensuring long-term durability of PSCs, compared with the pristine. The performance improvement is the result of highly efficient hole extraction because of the enhancement of electrical properties of PEDOT:PSS by Ag-CQD. It is also attributed to the improved perovskite crystallinity, which depends on the increased surface energy of HTL. This work provides an efficient route to realise more stable PSCs capable of delivering outstanding charge dynamics. [ABSTRACT FROM AUTHOR]