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Here we report the first example of a class of additively manufactured carbon fiber reinforced composite (AMCFRC) materials which have been achieved through the use of a latent thermal cured aromatic thermoset resin system, through an adaptation of direct ink writing (DIW) 3D-printing technology. We have developed a means of printing high performance thermoset carbon fiber composites, which allow the fiber component of a resin and carbon fiber fluid to be aligned in three dimensions via controlled micro-extrusion and subsequently cured into complex geometries. Characterization of our composite systems clearly show that we achieved a high order of fiber alignment within the composite microstructure, which in turn allows these materials to outperform equivalently filled randomly oriented carbon fiber and polymer composites. Furthermore, our AM carbon fiber composite systems exhibit highly orthotropic mechanical and electrical responses as a direct result of the alignment of carbon fiber bundles in the microscale which we predict will ultimately lead to the design of truly tailorable carbon fiber/polymer hybrid materials having locally programmable complex electrical, thermal and mechanical response.

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities ( R 2  > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

Observations of massive star-forming regions show that classical stellar wind models overpredict the luminosity of the X-ray-emitting gas, indicating a significant fraction of wind energy is lost. In this paper, we present a multiwavelength analysis of the giant H ii region 30 Doradus and its central star cluster R136 using 2 Ms of Chandra data, combined with James Webb Space Telescope and Hubble Space Telescope imaging and Spitzer spectral energy distributions, to investigate how the energy of the hot gas is lost through turbulent mixing, radiative cooling, and physical leakage. We compare the spatial and spectral properties of the hot gas with those of the warm ionized gas and dust. We find no significant correlation between the dust and hot gas temperatures, suggesting they are not directly coupled and that the dust resides in the swept-up shells where it is heated radiatively. Hα and X-ray surface brightness profiles show that the X-rays peak interior to the Hα shells, demonstrating partial confinement of the hot gas. The fragmented shell structure and the bright X-ray interior that declines near the Hα shell reflect efficient cooling from turbulent mixing at the hot–cold interface. We compare against recent simulations of stellar-feedback-driven bubbles, which have broad agreement with the morphology of the X-ray and Hα emission, but the simulations produce a dip in the interior X-ray surface brightness and a lack of hard X-rays compared to the observations. These differences may suggest thermal conduction is important because mass-loading of the hot bubble could reproduce the X-ray observables.

We present the first Chandra X-ray observations of H72.97–69.39, a highly embedded, potential superstar cluster in its infancy located in the star-forming complex N79 of the Large Magellanic Cloud. We detect particularly hard, diffuse X-ray emission that is coincident with the young stellar objects identified with JWST, and the hot gas fills cavities in the dense gas mapped by the Atacama Large Millimeter/submillimeter Array. The X-ray spectra are best fit with either a thermal plasma or power-law model, and assuming the former, we show that the X-ray luminosity of L X = (1.0 ± 0.3) × 1034 erg s−1 is a factor of ∼20 below the expectation for a fully confined wind bubble. Our results suggest that stellar wind feedback produces diffuse hot gas in the earliest stages of massive star cluster formation and that wind energy can be lost quickly via either turbulent mixing followed by radiative cooling or by physical leakage.

Shape memory polymers (SMPs) are attractive materials due to their unique mechanical properties, including high deformation capacity and shape recovery. SMPs are easier to process, lightweight, and inexpensive compared to their metallic counterparts, shape memory alloys. However, SMPs are limited to relatively small form factors due to their low recovery stresses. Lightweight, micro-architected composite SMPs may overcome these size limitations and offer the ability to combine functional properties (e.g., electrical conductivity) with shape memory behavior. Fabrication of 3D SMP thermoset structures via traditional manufacturing methods is challenging, especially for designs that are composed of multiple materials within porous microarchitectures designed for specific shape change strategies, e.g. sequential shape recovery. We report thermoset SMP composite inks containing some materials from renewable resources that can be 3D printed into complex, multi-material architectures that exhibit programmable shape changes with temperature and time. Through addition of fiber-based fillers, we demonstrate printing of electrically conductive SMPs where multiple shape states may induce functional changes in a device and that shape changes can be actuated via heating of printed composites. The ability of SMPs to recover their original shapes will be advantageous for a broad range of applications, including medical, aerospace, and robotic devices.

Osteoarthritis (OA) is the most common articular chronic disease. However, its current treatment is limited and mostly symptomatic. Hydrogen sulfide (H2S) is an endogenous gas with recognized physiological activities. The purpose here was to evaluate the effects of the intraarticular administration of a slow-releasing H2S compound (GYY-4137) on an OA experimental model. OA was induced in Wistar rats by the transection of medial collateral ligament and the removal of the medial meniscus of the left joint. The animals were randomized into three groups: non-treated and intraarticularly injected with saline or GYY-4137. Joint destabilization induced articular thickening (≈5% increment), the loss of joint mobility and flexion (≈12-degree angle), and increased levels of pain (≈1.5 points on a scale of 0 to 3). Animals treated with GYY-4137 presented improved motor function of the joint, as well as lower pain levels (≈75% recovery). We also observed that cartilage deterioration was attenuated in the GYY-4137 group (≈30% compared with the saline group). Likewise, these animals showed a reduced presence of pro-inflammatory mediators (cyclooxygenase-2, inducible nitric oxide synthase, and metalloproteinase-13) and lower oxidative damage in the cartilage. The increment of the nuclear factor-erythroid 2-related factor 2 (Nrf-2) levels and Nrf-2-regulated gene expression (≈30%) in the GYY-4137 group seem to be underlying its chondroprotective effects. Our results suggest the beneficial impact of the intraarticular administration of H2S on experimental OA, showing a reduced cartilage destruction and oxidative damage, and supporting the use of slow H2S-producing molecules as a complementary treatment in OA.

We investigate the relationship between star formation and X-ray emission from the hot interstellar medium (ISM) on ∼kiloparsec scales in NGC 4254 (M99) by combining spatially resolved star formation histories (SFHs) and Bayesian X-ray spectral fitting. We measure subgalactic star formation rates (SFR) by modeling spectrophotometric UV-IR data with flexible SFHs, and we produce point-source-subtracted maps of the diffuse X-ray emission using Chandra data. We extract and fit the spectra of five regions selected by their SFR density ΣSFR, deriving hot gas luminosities and plasma temperatures. We examine the subgalactic kT–ΣSFR and LXgas−ΣSFR scaling relations in NGC 4254 and compare to predictions from simple models of the feedback into the ISM from core collapse supernovae (CCSNe). The hot gas emission from NGC 4254 is consistent with thermalization of ≈40%–50% of the energy from CCSNe in the ISM, and mass-loading of the CCSNe ejecta, which decreases as ΣSFR−1/3 . Our optimized model implies a temperature and X-ray production efficiency that scale as kT=(0.72−0.18+0.26keV)ΣSFR0.34±0.10 and η=(0.03−0.01+0.02)ΣSFR0.34±0.18 , respectively, for ΣSFR = 0.01–0.13 M⊙ yr−1 kpc−2. We also compare the properties of the hot ISM to other ISM phases using data from the PHANGS program. The diffuse X-ray emission of a given region is on average 200 times fainter than the Hα emission, and we see evidence that the hot ISM is overpressurized compared to the large-scale dynamical equilibrium pressure of the galaxy, consistent with expansion of the hot ISM into the ambient medium.

Research is needed to understand and address barriers to risk management for women at high (≥20% lifetime) risk for breast cancer, but recruiting this population for research studies is challenging. This paper compares a variety of recruitment strategies used for a cross-sectional, observational study of high-risk women. Eligible participants were assigned female at birth, aged 25-85 years, English-speaking, living in the United States, and at high risk for breast cancer as defined by the American College of Radiology. Individuals were excluded if they had a personal history of breast cancer, prior bilateral mastectomy, medical contraindications for magnetic resonance imaging, or were not up-to-date on screening mammography per American College of Radiology guidelines. Participants were recruited from August 2020 to January 2021 using the following mechanisms: targeted Facebook advertisements, Twitter posts, ResearchMatch (a web-based research recruitment database), community partner promotions, paper flyers, and community outreach events. Interested individuals were directed to a secure website with eligibility screening questions. Participants self-reported method of recruitment during the eligibility screening. For each recruitment strategy, we calculated the rate of eligible respondents and completed surveys, costs per eligible participant, and participant demographics. We received 1566 unique responses to the eligibility screener. Participants most often reported recruitment via Facebook advertisements (724/1566, 46%) and ResearchMatch (646/1566, 41%). Community partner promotions resulted in the highest proportion of eligible respondents (24/46, 52%), while ResearchMatch had the lowest proportion of eligible respondents (73/646, 11%). Word of mouth was the most cost-effective recruitment strategy (US $4.66 per completed survey response) and paper flyers were the least cost-effective (US $1448.13 per completed survey response). The demographic characteristics of eligible respondents varied by recruitment strategy: Twitter posts and community outreach events resulted in the highest proportion of Hispanic or Latina women (1/4, 25% and 2/6, 33%, respectively), and community partner promotions resulted in the highest proportion of non-Hispanic Black women (4/24, 17%). Although recruitment strategies varied in their yield of study participants, results overall support the feasibility of identifying and recruiting women at high risk for breast cancer outside of clinical settings. Researchers must balance the associated costs and participant yield of various recruitment strategies in planning future studies focused on high-risk women.