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This study demonstrates the sustainable synthesis of multifunctional CIS@MIL-101(Cr) composites for water treatment applications. The composites were prepared via hybridization of CuInS2 with MIL-101(Cr) resulting in the formation of CIS nanoplates incorporated into MIL-101(Cr). The composites exhibited high visible light photocatalytic activity due to their low band gap energy and were tested for tetracycline photodegradation achieving a degradation efficiency of 98.8%. The material showed high stability after four cycles, and the effects of reactive species on photodegradation were investigated. The kinetics and mechanism of the photocatalytic process were studied, and HPLC-MS analysis was conducted to identify intermediate products. These results demonstrate the potential of using waste PET to create new semiconductors for water pollution control, promoting a circular material pathway.

Computations based on density functional theory (DFT) are transforming various aspects of materials research and discovery. However, the effort required to solve the central equation of DFT, namely the Kohn-Sham equation, which remains a major obstacle for studying large systems with hundreds of atoms in a practical amount of time with routine computational resources. Here, we propose a deep learning architecture that systematically learns the input-output behavior of the Kohn-Sham equation and predicts the electronic density of states, a primary output of DFT calculations, with unprecedented speed and chemical accuracy. The algorithm also adapts and progressively improves in predictive power and versatility as it is exposed to new diverse atomic configurations. We demonstrate this capability for a diverse set of carbon allotropes spanning a large configurational and phase space. The electronic density of states, along with the electronic charge density, may be used downstream to predict a variety of materials properties, bypassing the Kohn-Sham equation, leading to an ultrafast and high-fidelity DFT emulator.

The presence of bistable polarization states along with accessible switching capabilities make ferroelectrics an ideal candidate for a variety of applications. Although many conventional ferroelect...

Nanostructured high-temperature superconductors YBa2Cu3O6+δ and Bi2Sr2CaCu2O8+δ were synthesised using a melamine formaldehyde sponge as a sacrificial template, via three solution-based approaches. In the case of YBa2Cu3O6+δ, a modified Pechini method produced a material with a superconducting transition at 92 K and a specific surface area of 4.22 m2g-1. Further analysis with Hg porosimetry determined that the sponge exhibited a porosity of 82 %. In the case of Bi2Sr2CaCu2O8+δ, this method produced a material that exhibited superconductivity at 86 K with a specific surface area of 9.62 m2g-1. Hg-porosimetry determined that the BSCCO sponge exhibited a porosity of 78 %.

To facilitate chemical space exploration for material screening or to accelerate computationally expensive first-principles simulations, inexpensive surrogate models that capture electronic, atomistic, or macroscopic materials properties have become an increasingly popular tool over the last decade. The most fundamental quantity common across all such machine learning (ML)-based methods is the fingerprint used to numerically represent a material or its structure. To increase the learning capability of the ML methods, the common practice is to construct fingerprints that satisfy the same symmetry relations as displayed by the target material property of interest (for which the ML model is being developed). Thus, in this work, we present a general, simple, and elegant fingerprint that can be used to learn different electronic/atomistic/structural properties, irrespective of their scalar, vector, or tensorial nature. This fingerprint is based on the concept of multipole terms and can be systematically increa...

Breakdown strength, the maximum electric field that can be applied on a dielectric polymer without destroying its insulating characteristics, sets an upper limit on the maximum energy that can be stored in this material. Despite its significance, the breakdown strength remains poorly understood and impractical to compute. This is a major challenge in the development of high-energy dielectric polymers for which a large number of candidates must be screened for identifying those with high breakdown strength. In this work, we develop a multistep strategy for accessing the breakdown strength through two proxies that can be computationally estimated in a high-throughput manner, i.e., the polymer band gap and electron injection barrier at electrode-polymer interfaces. First, these properties are experimentally proven (established) to be correlated strongly with the breakdown strength of a number of benchmark polymers. Then, we develop a simple model, which relies on the chain structure of polymers, to estimate their band gap and electron injection barrier at the level of density functional theory. After validation, this model was finally used for 990 polymers, identifying 53 candidates that have preferable proxies, and thus, potentially having high breakdown strength. Because of the past synthesizability evidence of these polymers, we hope that they may be considered to be synthesized and tested in the near future. Moreover, some empirical rules that were extracted from our computed data could be useful for polymer selection and design in general. We note that the strategy used here is generic and can be used to design materials with other attractive, but complex, properties as well.

This article was migrated. The article was marked as recommended. Objective: To assess medical students' perspective on medical history embedded into a pre-existing learning module. Design, Setting, and Participants: This study was performed in 2018 at the University of Tasmania; participants were medical students in year three of the Bachelor of Medicine Bachelor of Surgery course. This was a cross-sectional study which used a mixed-method survey before and after a lecture series to assess medical students' perspectives on history of medicine. Intervention: Historical perspectives were incorporated into existing surgical pathology tutorials. Main outcome measures: Students completed a survey on medical history before and after the lecture series. The survey used both qualitative and quantitative measures to assess students' perception of the utility of medical history and how it was taught in this project. Results: In the initialquestionnaire, students indicated they believed medical history would help make them better doctors and enhance their learning of pathology. In the final questionnaire, students agreed that learning medical history was important in becoming a doctor. Students enjoyed the content and found the integration of history and pathology beneficial to learning. Conclusion: This study demonstrates one method by which to increase medial history teaching without major alterations to an existing medical curriculum.

Introducing hierarchical pore structure to microporous materials such as metal-organic frameworks (MOFs) can be beneficial for reactions where the rate of reaction is limited by low rates of diffusion or high pressure drop. This advantageous pore structure can be obtained by defect formation, mostly via post-synthetic acid etching, which has been studied extensively on water-stable MOFs. Here we show that a water-unstable HKUST-1 MOF can also be modified in a corresponding manner by using phosphoric acid as a size-selective etching agent and a mixture of dimethyl sulfoxide and methanol as a dilute solvent. Interestingly, we demonstrate that the etching process which is time- and acidity- dependent, can result in formation of defective HKUST-1 with extra interconnected hexagonal macropores without compromising on the bulk crystallinity. These findings suggest an intelligent scalable synthetic method for formation of hierarchical porosity in MOFs that are prone to hydrolysis, for improved molecular accessibility and diffusion for catalysis. Figure 1

Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.

The thermal properties of yttrium-stabilized lithium zirconate phosphate [LZP: Li1+x+yYxZr2−x(PO4)3 with x = 0.15, −0.2 ≤ y ≤ 0.4 and with x = 0.0, y = 0.0] are presented over a wide temperature range from 30 to 973 K, elucidating the interplay between structural phase transformations and thermal properties in a solid state superionic conducting material. At room temperature, the thermal conductivity decreases by more than 75% as the stoichiometry is changed from lithium deficient to excess and increases with increasing temperature, indicative of defect-mediated transport in the spark plasma sintered materials. The phase transformations and their stabilities are examined by x-ray diffraction and differential scanning calorimetry and indicate that the Y3+ substitution of Zr4+ is effective in stabilizing the ionically conductive rhombohedral phase over the entire temperature range measured, the mechanism of which is found through ab initio theoretical calculations. These insights into thermal transport of LZP superionic conductors are valuable as they may be generally applicable for predicting material stability and thermal management in the ceramic electrolyte of future all-solid-state-battery devices.

Analysis of risk is a problem has attracted a special deal of consideration from enterprises. This paper proposes the integrating the fuzzy logic and the improved fuzzy clustering algorithm FCM-R to build a hybrid method for analyzing the risk. The method has two stages. In stage 1, first, the fuzzy logic is used to determine the risk levels of objects. The next, the objects which have risk level is greater or equal a given threshold, are selected for analyzing in the next stage. In stage 2, the improved fuzzy clustering algorithm FCM-R is applied to the objects chosen to create the appropriate number of clusters which are ranked based on the risk level measure of clusters. Here, for illustrating, we choose objects to analyze risk are customers in the enterprise. The proposed method has been experimented with real data set to generate customer clusters ranked according to the risk level measure from high to low. The results will be to use for predicting the customer’s risk and will be to help for offering the risk management policies to avoid loss.

Analysis of customer risk is important task in business operation of the enterprise. This paper, first proposes the building of strength measure to rank clusters obtained from clustering algorithm. The next step builds an improved clustering algorithm by integrating of computing this measure into a clustering algorithm. This improved clustering algorithm is named Fuzzy C-Means-Rank (FCM-R). The final step proposes a hybrid data mining method to analyze the risk of the customer. The hybrid method includes two stages. In the first stage, we apply a classifying algorithm to classify customers into two groups: risk and no risk. In the second stage, we apply the improved clustering algorithm FCM-R to cluster risk customers into clusters and to rank clusters by measuring the risk level of clusters. The hybrid method has been applied to a real data set to generate clusters ranked according to the risk level from high to low. With such results, our proposed method will support enterprises to analyze customer risk and to propose appropriate risk management regulation for customers in each cluster.

A difficult problem of most clustering algorithms is how to specify the appropriate number of clusters. This paper proposes an adaptive method of selecting of number of clusters in clustering process by making coefficients indicated the appropriate number of clusters. The intra-cluster coefficient reflects intra distortion of cluster through maximum distance and a mean distance of cluster's extremely marginal objects. The inter-cluster coefficient reflects distance among clusters. It is ratio between closest distance from this cluster's centre to an extremely marginal object of other cluster and mean distance from this cluster's centre to all of extremely marginal object of other cluster respectively. A new coefficient that indicates the appropriate number of clusters is build from the intra-cluster coefficient and inter-cluster coefficient. The looking for extremely marginal objects and the new coefficient are integrated in a weighted FCM algorithm and it is calculated adaptively while the weighted FCM is processing. The weighted FCM algorithm integrated new coefficient is called FCM++. We experiment with FCM++ on some data sets of UCI: Iris, Wine, Soybean-small and show encouraging results.

The recent empirical observation of ferroelectricity in hafnia is rather surprising since all of its known ground-state phases are nonpolar. In this letter, we show that finite size effects, relevant under experimental conditions, may themselves lead to this unexpected phenomenon due to stabilization of metastable polar phase(s). Using surface energies computed from first principles, we determine the thermodynamic stability of a parallelepiped shaped particle constructed from various low energy nonpolar and polar phases of hafnia. We find that at small dimensions, surface effects may stabilize either one of the polar phases or the nonpolar tetragonal phase (the parent phase of the polar phases), suggesting a possible explanation of the ferroelectric behavior observed in pure hafnia films. These results also explain the stabilization of the metastable tetragonal phase in nanoparticles of zirconia, the twin oxide of hafnia. While a comprehensive understanding of the origins of ferroelectricity in hafnia will require inclusion of other relevant factors (such as strain and dopants), this work highlights the importance of finite size effects as a possible key factor.