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Supersoft Norbornene‐Based Thermoplastic Elastomers with High Strength and Upper Service TemperatureWith over 6 million tons produced annually, thermoplastic elastomers (TPEs) have become ubiquitous in modern society, due to their unique combination of elasticity, toughness, and reprocessability. Nevertheless, industrial TPEs display a tradeoff between softness and strength, along with low upper service temperatures, typically ≤100 °C. This limits their utility, such as in bio‐interfacial applications where supersoft deformation is required in tandem with strength, in addition to applications that require thermal stability (e.g., encapsulation of electronics, seals/joints for aeronautics, protective clothing for firefighting, and biomedical devices that can be subjected to steam sterilization). Thus, combining softness, strength, and high thermal resistance into a single versatile TPE has remained an unmet opportunity. Through de novo design and synthesis of novel norbornene‐basedmore » « less
ABA triblock copolymers, this gap is filled. Ring‐opening metathesis polymerization is employed to prepare TPEs with an unprecedented combination of properties, including skin‐like moduli (<100 kPa), strength competitive with commercial TPEs (>5 MPa), and upper service temperatures akin to high‐performance plastics (≈260 °C). Furthermore, the materials are elastic, tough, reprocessable, and shelf stable (≥2 months) without incorporation of plasticizer. Structure–property relationships identified herein inform development of next‐generation TPEs that are both biologically soft yet thermomechanically durable.Free, publicly-accessible full text available July 1, 2025 -
Abstract High reproductive compatibility between crops and their wild relatives can provide benefits for crop breeding but also poses risks for agricultural weed evolution. Weedy rice is a feral relative of rice that infests paddies and causes severe crop losses worldwide. In regions of tropical Asia where the wild progenitor of rice occurs, weedy rice could be influenced by hybridization with the wild species. Genomic analysis of this phenomenon has been very limited. Here we use whole genome sequence analyses of 217 wild, weedy and cultivated rice samples to show that wild rice hybridization has contributed substantially to the evolution of Southeast Asian weedy rice, with some strains acquiring weed-adaptive traits through introgression from the wild progenitor. Our study highlights how adaptive introgression from wild species can contribute to agricultural weed evolution, and it provides a case study of parallel evolution of weediness in independently-evolved strains of a weedy crop relative.
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Free, publicly-accessible full text available February 15, 2025
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Abstract Weedy rice is a close relative of cultivated rice that devastates rice productivity worldwide. In the southern United States, two distinct strains have been historically predominant, but the 21stcentury introduction of hybrid rice and herbicide resistant rice technologies has dramatically altered the weedy rice selective landscape. Here, we use whole-genome sequences of 48 contemporary weedy rice accessions to investigate the genomic consequences of crop-weed hybridization and selection for herbicide resistance. We find that population dynamics have shifted such that most contemporary weeds are now crop-weed hybrid derivatives, and that their genomes have subsequently evolved to be more like their weedy ancestors. Haplotype analysis reveals extensive adaptive introgression of cultivated alleles at the resistance gene
ALS , but also uncovers evidence for convergent molecular evolution in accessions with no signs of hybrid origin. The results of this study suggest a new era of weedy rice evolution in the United States. -
Abstract We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 <
z < 1.13, which is the largest sample of supernovae from any single survey and increases the number of knownz > 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are 0.017 in a flat ΛCDM model, and = (0.082, 0.152) in a flatw CDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time. -
Context. The determination of accurate photometric redshifts (photo-zs ) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs .Aims. The goal of this work is to calculate the photo-zs for the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach.Methods. We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-z computation on the Y3 DES Deep Fields with DNF and estimated its performance.Results. The photo-zs of the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zs in deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) andEuclid data, which will have similar depths to the Y3 DES Deep Fields.Free, publicly-accessible full text available June 1, 2025