Search for: All records

Abstract Intra‐articular injections of hyaluronic acid (HA) are the cornerstone of osteoarthritis (OA) treatments. However, the mechanism of action and efficacy of HA viscosupplementation are debated. As such, there has been recent interest in developing synthetic viscosupplements. Recently, a synthetic 4 wt% polyacrylamide (pAAm) hydrogel was shown to effectively lubricate and bind to the surface of cartilage in vitro. However, its ability to localize to cartilage and alter the tribological properties of the tissue in a live articulating large animal joint is not known. The goal of this study was to quantify the distribution and extent of localization of pAAm in the equine metacarpophalangeal or metatarsophalangeal joint (fetlock joint), and determine whether preferential localization of pAAm influences the tribological properties of the tissue. An established planar fluorescence imaging technique was used to visualize and quantify the distribution of fluorescently labeled pAAm within the joint. While the pAAm hydrogel was present on all surfaces, it was not uniformly distributed, with more material present near the site of the injection. The lubricating ability of the cartilage in the joint was then assessed using a custom tribometer across two orders of magnitude of sliding speed in healthy synovial fluid. Cartilage regions with a greater coverage of pAAm, that is, higher fluorescent intensities, exhibited friction coefficients nearly 2‐fold lower than regions with lesser pAAm (R_rm = −0.59,p < 0.001). Collectively, the findings from this study indicate that intra‐articular viscosupplement injections are not evenly distributed inside a joint, and the tribological outcomes of these materials is strongly determined by the ability of the material to localize to the articulating surfaces in the joint. 

This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combines multiple techniques to develop a feature set encompassing all relative aspects of polymer chemistry, to extract and explain correlations between features and Tg, and to develop and apply a high-throughput predictive model. In this work, we identify aspects of the chemistry that most impact Tg, including a parameter related to rotational degrees of freedom and a backbone index based on a steric hindrance parameter. Building on this scientific understanding, models are developed on different types of data to ensure robustness, and experimental validation is obtained through the testing of new polymer chemistry with remarkable Tg. The ability of our model to predict Tg shows that the relevant information is contained within the topological descriptors, while the requirement of non-linear manifold transformation of the data also shows that the relationships are complex and cannot be captured through traditional regression approaches. Building on the scientific understanding obtained from the correlation analyses, coupled with the model performance, it is shown that the rigidity and interaction dynamics of the polymer structure are key to tuning for achieving targeted performance. This work has implications for future rapid optimization of chemistries 

Abstract Climate change is causing rapid warming in the Arctic, which, alongside other physical, socio-economic, cultural, geopolitical, and technological factors, is driving change in the far north. This research presents a conceptual model summarizing Arctic change factors which in turn was used in the design of a Delphi exercise which leveraged a variety of experts to forecast trajectories in different parts of the Arctic. Based on these experts’ expectations for economic and governance outcomes by 2050, we find that our results illustrate the “many Arctics” concept or some of the ways in which the Arctic is heterogenous now, and perhaps becoming increasingly so in the future. Sub-regions of the Arctic differed in expert expectations about the future of resource extraction, tourism, Indigenous self-determination, and military activity, among other outcomes. This work also discusses the post-2022 geopolitical situation and some potential implications of “many Arctics” for policy and future governance. 

Abstract The Channeled Scabland of eastern Washington (USA) was formed by outburst floods from glacial Lake Missoula. Despite chronological advances, the timing of erosion in the main flood channels is unresolved. In particular, it is still uncertain whether upper Grand Coulee, the largest canyon in the Channeled Scabland, was incised during or prior to the last glaciation. We report 10Be exposure ages from erratics in upper Grand Coulee, glacial Lake Columbia, and surrounding flood routes. Flood-transported boulders on the high-elevation east rim of Grand Coulee date to ca. 17–15 ka. Ages from boulders on the floor of Grand Coulee indicate later flooding at ca. 14 ka, which post-dated canyon incision and occurred after inundation of the Telford-Crab Creek scabland at ca. 15–14.5 ka. Prior hydraulic modeling and dating suggest the entrance to Grand Coulee was blocked by rock and that canyon incision was incomplete at ca. 17 ka; hence, we interpret the 17–15 ka exposure ages on the east rim to coincide with flow over a retreating cataract during canyon incision. Our results indicate incision of Grand Coulee was completed between 17 ka and 14 ka. The short duration of canyon incision suggests that glacial Lake Missoula generated some of the most erosive outburst floods in Earth's history. 

This manuscript describes a simple and effective method to cyclodehydrogenate arenes using liquid alkali metals. Direct reaction between molten potassium and arenes forms 6-membered rings and zigzag edged structures within polyarenes. The approach is extended to integration of pyridinic nitrogen and 5-membered rings to arene structures and synthesis of larger, open-shell nanographenes.