Search for: All records

This study details the enhancement of CO2 selectivity in ring opening metathesis polymerization (ROMP) polymers that contain nitrile moieties and micro-pore generating ladder side chains. A material, CN-ROMP homopolymer, with nitriles in the ladder side chains was originally targeted and synthesized, however its low molecular weight and backbone rigidity precluded film formation. As a result, an alternative method was pursued wherein copolymers were synthesized using norbornene (N) and nitrile norbornene (NN). Herein, we report an investigation of the structure–property relationships of backbone functionalization and grafting density on the CO2 transport properties in these ROMP polymers. Nitrile-containing copolymers showed an increase in CO2/CH4 sorption selectivity and a concomitant increase in CO2/CH4 permselectivity when compared to the unfunctionalized (nitrile free) analogs. The stability in CO2 rich environments is enhanced as grafting density of the rigid, pore-generating side chains increases and an apparent tunability of CO2 plasticization pressure was observed as a function of norbornene content. Lower loadings of norbornene resulted in higher plasticization pressure points. Gas permeability in the ROMP copolymers was found to correlate most strongly with the concentration of ladder macromonomers in the polymer chain. 

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

 

Climate anomalies and changes have complex and critical impacts on agriculture. Given global warming, the scientific community has dramatically increased research on these impacts. During 1996–2022, over 3,000 peer-reviewed papers in the Web of Science Core Collection database have investigated the fields. This study conducted a bibliometric analysis of these papers for systematic mapping and inductive understanding to comprehensively review the research’s status, focus, network, and funding. After almost 30 years, the research is now centered in quantifying climate impacts on crop yields and agriculture productivity while seeking effective adaptation solutions. The hot keywords recently emerged include poverty, food security, water resource, climate service, climate-smart agriculture, sustainability, and policy. They suggest increasing concerns on global food and water shortage and pressing needs for action to adapt to climate change and sustain agricultural productivity. Given the uncertainty of climate change and the complexity of agriculture systems, most current studies are interdisciplinary research combining various agricultural fields with climate, environmental, and socioeconomic sciences. The United States, as the world’s leading food commodity producer, has the most diverse funding agencies and provides the largest number of awards to support the research. Future priority research should take the coupled earth system approach with the food-energy-water nexus principles to provide effective, actionable decision supports at local-regional scales to sustain national agricultural productivity and quantify climate-smart agricultural practices to mitigate global warming.

 

Graphene oxide/polymer composite water filtration membranes were developed via coalescence of graphene oxide (GO) stabilized Pickering emulsions around a porosity-generating polymer. Triptycene poly(ether ether sulfone)-CH2NH2:HCl polymer interacts with the GO at the water−oil interface, resulting in stable Pickering emulsions. When they are deposited and dried on polytetrafluoroethylene substrate, the emulsions fuse to form a continuous GO/polymer composite membrane. X-ray diffraction and scanning electron microscopy demonstrate that the intersheet spacing and thickness of the membranes increased with increasing polymer concentration, confirming the polymer as the spacer between the GO sheets. The water filtration capability of the composite membranes was tested by removing Rose Bengal from water, mimicking separations of weak black liquor waste. The composite membrane achieved 65% rejection and 2500 g m−2 h−1 bar−1. With high polymer and GO loading, composite membranes give superior rejection and permeance performance when compared with a GO membrane. This methodology for fabrication membranes via GO/polymer Pickering emulsions produces membranes with a homogeneous morphology and robust chemical separation strength. 

Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

 

We present ReasonBert, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBert achieves remarkable improvement over an array of strong baselines. Few-shot experiments further demonstrate that our pre-training method substantially improves sample efficiency. 

In settings where an outcome, a decision, or a statement is made based on a single option among alternatives, it is popular to cherry-pick the data to generate an outcome that is supported by the cherry-picked data but not in general. In this paper, we use perturbation as a technique to design a support measure to detect, and resolve, cherry-picking across different contexts. In particular, to demonstrate the general scope of our proposal, we study cherry picking in two very different domains: (a) political statements based on trend-lines and (b) linear rankings. We also discuss sampling-based estimation as an effective and efficient approximation approach for detecting and resolving cherry-picking at scale.