Mayaterials (United States)

Recently, the development of high-performance anti-reflective and self-cleaning (AR-SC) coatings for perovskite solar cells (PSCs) has attracted considerable attention. However, conventional AR-SC coatings typically rely on high-temperature sintering, complex nanostructuring, vacuum deposition, or photolithography─processes that are costly, difficult to scale, and often limited to a single functionality. In this work, we present a novel low-temperature-processable and mechanically robust composite coating comprising methyl methacrylate and silicone resin (MMA@MQ), which is prepared via simple magnetic stirring and applied without energy-intensive curing. When optimized and deposited by spin-coating, the MMA@MQ layer markedly improves optical transmittance (from 79.02 to 85.94% on FTO) and confers strong hydrophobicity (water contact angle increased from 21.14 to 103.97°). Integrated into PSCs, it increases the power conversion efficiency from 20.27 to 21.25%. Importantly, we demonstrate the feasibility of scalable fabrication using industry-compatible methods such as dip-coating and scraping, achieving a similar performance. We further propose a “Performance Triangle” to guide future industrial development of AR-SC coatings, balancing optical, self-cleaning, and stability metrics for maximal energy yield in real-world photovoltaic applications. This work offers a facile, scalable, and low-cost strategy to simultaneously achieve multiple functions and durability of AR-SC coatings, thereby facilitating their commercialization.

Abstract To combat antibiotic-resistant priority pathogens such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus faecium, novel antibiotics are urgently needed1-3. DNA polymerases, essential for DNA replication but structurally distinct between bacteria and eukaryotes, represent promising yet underexplored targets4,5. The replicative polymerase in Gram-positive bacteria, PolC, is specifically inhibited by ibezapolstat, a guanine nucleobase analogue with an aromatic modification at the N2-position6,7. Following the success of ibezapolstat against Clostridioides difficile infections (CDI) in Phase 2 studies8,9, next-generation compounds with distinct pharmacokinetic properties are being designed to target other Gram-positive pathogens10. However, mechanistic insight into the mode of action of PolC inhibitors is lacking. Here, using cryo-electron microscopy, we show that ibezapolstat and next-generation inhibitor ACX-801 use the nucleobase analogue to pair with DNA within the active site of E. faecium PolC, similar to dGTP. The N2-linked aromatic group is positioned perpendicularly to the nucleobase and inserted into an induced binding pocket. Our results explain reduced susceptibility due to mutation of F1276, that plays a pivotal role in the creation of this pocket and engages in interactions with both the aromatic and nucleobase moieties. This work lays the foundation for rational development of an innovative class of antimicrobials against Gram-positive priority pathogens.

This study explores the concept of luxury through the lens of sustainability, questioning conventional interpretations and promoting a broader comprehension of luxury’s compatibility with sustainability. It also explores the potential positive effects of luxury consumption and production at various scales—individual (micro), industry (meso), and environment (macro). This research, adopting a positive theory approach and aligning with the transformative luxury research (TLR) stream, proposes a novel framework termed “positive luxury.” This framework, informed by consumer insights, introduces additional pillars of sustainable practices in the luxury sector that yield positive impacts. These pillars encompass ecological, social, and economic aspects, as well as ethical governance, supply chain integrity, philanthropy, well-being, and diversity, equality, and inclusion (DEI). The findings underscore the significance of a consumer-centered perspective and reveal the nuanced positive impacts that consumers perceive from each of these pillars. Furthermore, the study advances existing works by classifying sustainable pillars into either “core” or “incipient” qualities, while responding to the TLR stream’s call for a focus on well-being outcomes. The findings have implications for policymakers and luxury businesses alike, offering potential directions for future research.

We report an effective piezoelectric coefficient of deff = 40 ± 6 pm/V in the paraelectric phase of (1-x)Pb(Mg1/3Nb2/3)O3−(x)PbTiO3 single crystals with x = 0.32 (PMN-32PT), which is near the morphotropic phase boundary (x = 0.30). This value for deff in the paraelectric phase is significantly larger than that for other ferroelectrics measured so far. Our detailed investigation of symmetry-disallowed piezoelectricity indicates bias effects due to alternating current (AC) electric fields (100–800 kHz) and AC field cooling. The application of moderate AC fields (100–350 V/cm) gives rise to a nonlinear response of the piezoelectric effect, leading to an irreversible increase in the piezoelectric coefficient in the paraelectric phase. Along with changes in the elastic properties, the observed poling due to AC electric fields is reminiscent of irreversible ferroelectric–ferroelastic domain wall motion in ferroelectric phases. Such behavior is absent in cubic SrTiO3 where precursor effects are commonly observed at much lower temperatures.

Recent advancements in machine learning (ML) have revolutionized the field of high-performance materials design. However, developing robust ML models to decipher intricate structure-property relationships in materials remains challenging, primarily due to the limited availability of labeled datasets with well-characterized crystal structures. This is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic symmetry. We introduce a self-supervised probabilistic model (SSPM) that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures, utilizing solely the existing crystal structure data from materials databases. SSPM significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal structure. We showcase SSPM’s capability by discovering shape memory alloys (SMAs). Amongst the top 50 predictions, 23 have been confirmed as SMAs either experimentally or theoretically, and a previously unknown SMA candidate, MgAu, has been identified.

Abstract Finding high temperature superconductors (HTS) has been a continuing challenge due to the difficulty in predicting the transition temperature (Tc) of superconductors. Recently, the efficiency of predicting Tc has been greatly improved via machine learning (ML). Unfortunately, prevailing ML models have not shown adequate generalization ability to find new HTS, yet. In this work, a graph neural network model is trained to predict the maximal Tc (Tcmax) of various materials. Our model reveals a close connection between Tcmax and chemical bonds. It suggests that shorter bond lengths are favored by high Tc, which is in coherence with previous domain knowledge. More importantly, it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc, which is new even to the human experts. It can provide a convenient guidance to the materials scientists in search of HTS.