Nature最新文献

In the past decade, moiré materials have revolutionized how we engineer and control quantum phases of matter^1,2. They are versatile platforms for strongly correlated electronic phenomena^3,4 and support new ferroelectric^5,6, magnetic⁷ and superconducting states⁸. Among incommensurate materials⁹, moiré materials are aperiodic composite crystals^10,11 whose long-wavelength superlattices enable tunable properties without chemically modifying their layers. So far, nearly all reports of moiré materials have investigated van der Waals heterostructures assembled far from thermodynamic equilibrium (T < 150 °C)^1,2. Here we introduce a conceptually new approach to synthesizing high-mobility moiré materials in thermodynamic equilibrium. We report a new family of foliated superlattice materials (Sr₆TaS₈)_1+δ(TaS₂)₈ that are exfoliatable, incommensurate-lattice, van der Waals crystals. Lattice mismatches between alternating layers generate moiré superlattices, analogous to 2D moiré heterobilayer superlattices, which are coherent throughout these crystals and tunable through synthesis conditions without altering their chemical composition. Quantum oscillation measurements map the complex Fermiology of these moiré metals^12-14, showing that the Fermi surface of the structurally simplest moiré metal comprises more than 40 distinct cross-sectional areas. This is naturally understood by proposing that these bulk moiré metals encode electronic properties of higher-dimensional superspace crystals in ways paralleling well-established crystallographic methods for incommensurate lattices^15,16. More broadly, our work demonstrates a scalable synthesis approach potentially capable of producing large-area moiré materials for electronics applications and evidences a new material design concept for accessing phenomena proposed in higher dimensions^17-21.

The three-dimensional architecture of natural habitats is a key determinant of species biodiversity, harvestable biomass and resilience to disturbance^1,2. Indeed, some species, including trees, corals and oysters, alter resource availability and modify biotic and abiotic pressures through their own three-dimensional structures-thereby enhancing their own survival^3,4. However, which aspects of the three-dimensional architecture of these ecosystem engineers shape ecosystem dynamics and species survival are rarely examined by empirical studies, leaving much of the broader ecological and conservation impact of ecosystem engineering underexplored^4-6. Here we show that oyster reefs have combinations of geometric variables that maximize recruit survival, which is a key factor influencing oyster reef growth and persistence. Using three-dimensional habitat designs that capture the full spectrum of natural oyster reef architectures, as well as a geometric theory⁷ that links habitat surface area, fractal dimension and height, we show that oyster settlement and survival are greatest at particular combinations of fractal dimension and height that minimize predation. Our study provides a template for understanding optimal three-dimensional habitat configurations for habitat restoration projects that are proliferating globally, without targeting key architectural features of habitat space that maximize restoration success^8,9.

Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and have critical roles in virtually every eukaryotic cellular process¹. IDRs exist in a broad ensemble of structurally distinct conformations. This structural plasticity facilitates diverse molecular recognition and function^2-4. Here we combine advances in physics-based force fields with the power of multi-modal generative deep learning to develop STARLING, a framework for rapid generation of accurate IDR ensembles and ensemble-aware representations from sequence. STARLING supports environmental conditioning across ionic strengths and demonstrates proof of concept for the interpolative ability of generative models beyond their training domain. Moreover, we enable ensemble refinement under experimental constraints using a Bayesian maximum-entropy reweighting scheme. Beyond ensemble characterization, STARLING sequence representations can be used in multiple ways. We showcase two examples: first, STARLING lets us perform ensemble-based search for 'biophysical look-alikes'. Second, we demonstrate how these latent representations can be used to accelerate ensemble-first sequence design from weeks or hours per candidate to seconds, enabling library-scale designs. Together, STARLING dramatically lowers the barrier to the computational interrogation of IDR function through the lens of emergent biophysical properties, complementing bioinformatic protein sequence analysis. We evaluate the accuracy of STARLING against extant experimental data and offer a series of vignettes illustrating how STARLING can enable rapid hypothesis generation for IDR function and aid the interpretation of experimental data.

Colloidal perovskite nanocrystal (PeNC) has long been synthesized using the hot-injection method and room-temperature ligand-assisted reprecipitation as the prominent techniques^1,2. However, both methods have challenges for industrial-scale production^3-5: the hot-injection method requires high temperatures, an inert gas environment and rapid cooling, which raise safety concerns, whereas ligand-assisted reprecipitation can exhibit limited productivity on scale-up. Here we present a cold-injection method based on pseudo-emulsion, enabling scalable synthesis of PeNCs with near-unity photoluminescence quantum yield (PLQY, ~100%) and enhanced stability by injecting precursor solution below 4 °C. In the cold-injection method, PeNCs grow through the assembly of fully coordinated plumbates out of the pseudo-emulsion with the assistance of a demulsifier. We discovered that slow assembly of polybromide plumbates, assisted by cold temperature, is essential for defect suppression, resulting in reproducible, stable and pure-green-emitting PeNCs with near-unity PLQY. Furthermore, this method enables efficient large-scale production, achieving 20-l-scale synthesis with remarkable batch weight while maintaining near-unity PLQY. Our findings represent a substantial advancement in synthesis of high-quality PeNCs, offering potential for broad applications in display and lighting industries.

Triple-negative breast cancer (TNBC) is frequently associated with metastatic relapse, even at an early stage¹. Here we assessed an individualized neoantigen mRNA vaccine in 14 patients with TNBC following surgery and after neoadjuvant or adjuvant therapy. In peripheral blood of nearly all patients, high-magnitude, vaccine-induced, mostly de novo T cell responses to multiple neoantigens were detected that remained functional for several years. Characterization of individual patients revealed that a large proportion of these T cells developed into two subsets: a late-differentiated phenotype with markers indicative of 'ready-to-act' cytotoxic effector T cells, and T cells with a stem cell-like memory phenotype. Eleven patients remained relapse-free for up to six years post-vaccination. Recurrence occurred in three patients: the individual with the weakest vaccine-induced T cell response relapsed, but achieved complete remission on subsequent anti-PD-1 therapy; another patient had a tumour with low major histocompatibility complex (MHC) class I expression with MHC class I-deficient cells growing out under vaccination; and the third patient was BRCA-positive and had a recurrence from a genetically distinct primary tumour. These findings demonstrate the feasibility of individualized RNA vaccines in TNBC, document persistence of vaccine-induced, functional neoantigen-specific T cells and provide insights into possible immune escape mechanisms that will guide future approaches.