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Abstract Tellurene, a chiral chain semiconductor with a narrow bandgap and exceptional strain sensitivity, emerges as a pivotal material for tailoring electronic and optoelectronic properties via strain engineering. This study elucidates the fundamental mechanisms of ultrafast laser shock imprinting (LSI) in two-dimensional tellurium (Te), establishing a direct relationship between strain field orientation, mold topology, and anisotropic structural evolution. This is the first demonstration of ultrafast LSI on chiral chain Te unveiling orientation-sensitive dislocation networks. By applying controlled strain fields parallel or transverse to Te’s helical chains, we uncover two distinct deformation regimes. Strain aligned parallel to the chain’s direction induces gliding and rotation governed by weak interchain interactions, preserving covalent intrachain bonds and vibrational modes. In contrast, transverse strain drives shear-mediated multimodal deformations—tensile stretching, compression, and bending—resulting in significant lattice distortions and electronic property modulation. We discovered the critical role of mold topology on deformation: sharp-edged gratings generate localized shear forces surpassing those from homogeneous strain fields via smooth CD molds, triggering dislocation tangle formation, lattice reorientation, and inhomogeneous plastic deformation. Asymmetrical strain configurations enable localized structural transformations while retaining single-crystal integrity in adjacent regions—a balance essential for functional device integration. These insights position LSI as a precision tool for nanoscale strain engineering, capable of sculpting 2D material morphologies without compromising crystallinity. By bridging ultrafast mechanics with chiral chain material science, this work advances the design of strain-tunable devices for next-generation electronics and optoelectronics, while establishing a universal framework for manipulating anisotropic 2D systems under extreme strain rates. This work discovered crystallographic orientation-dependent deformation mechanisms in 2D Te, linking parallel strain to chain gliding and transverse strain to shear-driven multimodal distortion. It demonstrates mold geometry as a critical lever for strain localization and dislocation dynamics, with sharp-edged gratings enabling unprecedented control over lattice reorientation. Crucially, the identification of strain field conditions that reconcile severe plastic deformation with single-crystal retention offers a pathway to functional nanostructure fabrication, redefining LSI’s potential in ultrafast strain engineering of chiral chain materials. 

Antagonistic interactions are critical determinants of microbial community stability and composition, offering host benefits such as pathogen protection and providing avenues for antimicrobial control. While the ability to eliminate competitors confers an advantage to antagonistic microbes, it often incurs a fitness cost. Consequently, many microbes only produce toxins or engage in antagonistic behavior in response to specific cues like quorum sensing molecules or environmental stress. In laboratory settings, antagonistic microbes typically dominate over sensitive ones, raising the question of why both antagonistic and nonantagonistic microbes are found in natural environments and host microbiomes. Here, using both theoretical models and experiments with killer strains ofSaccharomyces cerevisiae, we show that “boom-and-bust” dynamics—periods of rapid growth punctuated by episodic mortality events—caused by temporal environmental fluctuations can favor nonantagonistic microbes that do not incur the growth rate cost of toxin production. Additionally, using control theory, we derive bounds on the competitive performance and identify optimal regulatory toxin-production strategies in various boom-and-bust environments where population dilutions occur either deterministically or stochastically over time. Our mathematical investigation reveals that optimal toxin regulation is much more beneficial to killers in stochastic, rather than deterministic, boom-and-bust environments. Overall, our findings show how both antagonistic and nonantagonistic microbes can thrive under varying environmental conditions. 

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative “cost” of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a “budget”). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such “threshold-aware” optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new “threshold-aware” policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. 

Antagonistic interactions are critical determinants of microbial community stability and composition, offering host benefits such as pathogen protection and providing avenues for antimicrobial control. While the ability to eliminate competitors confers an advantage to antagonistic microbes, it often incurs a fitness cost. Consequently, many microbes only produce toxins or engage in antagonistic behavior in response to specific cues like quorum sensing molecules or environmental stress. In laboratory settings, antagonistic microbes typically dominate over sensitive ones, raising the question of why both antagonistic and non-antagonistic microbes are found in natural environments and host microbiomes. Here, using both theoretical models and experiments with killer strains ofSaccharomyces cerevisiae, we show that boom-and-bust dynamics caused by temporal environmental fluctuations can favor non-antagonistic microbes that do not incur the growth rate cost of toxin production. Additionally, using control theory, we derive bounds on the competitive performance and identify optimal regulatory toxin-production strategies in various boom- and-bust environments where population dilutions occur either deterministically or stochastically over time. Our findings offer a new perspective on how both antagonistic and non-antagonistic microbes can thrive under varying environmental conditions. 

Abstract Relativistic Weyl fermion quasiparticles in Weyl semimetal bring the electron’s chirality degree of freedom into the electrical transport and give rise to exotic phenomena. A topological phase transition from a topological trivial phase to a topological non-trivial phase offers a route to control electronic devices through its topological properties. Here, we report the Weyl semimetal phase in hydrothermally grown two-dimensional Tellurium (2D Te) induced by high hydrostatic pressure (up to 2.47 GPa). The unique chiral crystal structure gives rise to chiral fermions with different topological chiral charges ($${{C}}=-{{1}},+{{1}},{{and}}-{{2}}$$ $C=-1,+1,and-2$). The highly tunable chemical potential in 2D Te provides comprehensive information for understanding the pressure-dependent electron band structure. The pressure-induced insulator-to-metal transition, two-carrier transport, and the non-trivial π Berry phase shift in quantum oscillations are observed in the 2D Te Weyl semimetal phase. Our work demonstrates the pressure-induced bandgap closing in the inversion asymmetric narrow bandgap semiconductor 2D Te. 

Abstract Dimethyl sulfide (DMS; CH₃SCH₃) from marine phytoplankton is a notable source of atmospheric sulfur¹. Its oxidation products include sulfuric acid (SA; H₂SO₄) and methanesulfonic acid (MSA; CH₃SO₃H), which has a higher yield than SA below 10 °C (ref. ²). Although SA is known to drive the formation of new particles³, which may subsequently grow and act as cloud condensation nuclei (CCN), the role of MSA remains unclear⁴. Here, in experiments performed under atmospheric conditions at the CERN CLOUD (Cosmics Leaving OUtdoor Droplets) chamber, we show that MSA nucleates together with ammonia (NH₃) below −10 °C, at rates comparable with SA-NH₃. Moreover, MSA and SA nucleate synergistically below −10 °C, forming multi-acid molecular clusters with NH₃. Even at ultralow NH₃levels, MSA drives particle growth at or near the kinetic limit below 9 °C and above 40% relative humidity (RH). Because MSA and SA generally coexist at similar concentrations in cool marine regions, our findings indicate that nucleation rates may be accelerated up to tenfold and growth rates up to twofold compared with SA-NH3 alone. Our global model simulations indicate that MSA can enhance CCN concentrations, especially in polar regions. We propose that MSA might be an important driver of biogenic particles in cool, pristine marine regions of both the present-day and pre-industrial atmospheres and yet is unaccounted for in global climate models⁵. 

Abstract. Laboratory experiments addressing complex phenomena such as atmospheric new-particle formation and growth typically involve numerous instruments measuring a range of key coupled variables. In addition to independent calibration, the combined dataset provides not just constraints on the parameters of interest but also on the critical instrument calibrations. Here we find good agreement between production and loss rates of sulfuric acid (H2SO4) in an experiment performed at the CERN CLOUD chamber involving oxidation of sulfur dioxide (SO2) in the presence of ammonia (NH3) at 58 % relative humidity, driving new-particle formation and growth of particles by H2SO4+NH3 nucleation initiated by O3 photolysis via several light sources. This closure requires consistency across numerous parameters, including: the particle number and size distribution; their condensation sink for H2SO4; the particle growth rates; the concentration of H2SO4; and the nucleation coefficients for both neutral and ion-induced pathways. Our study shows that accurate agreement can be achieved between production and loss of condensable vapors in laboratory chambers under atmospheric conditions, with accuracy ultimately tied to particle number measurement (i.e. a condensation particle counter). This, in turn implies parameters such as the H2SO4 concentration and particle size distributions can be determined to a comparable precision.