Designing a multimetallic catalyst based on the principle of metalloenzymes has emerged as a promising route to enhance catalytic performance owing to the combined action of different metal centers in a cooperative fashion. The cooperativity of metal centers with the surrounding ligand environment in metalloenzymes plays a crucial role in driving efficient catalysis. However, the replication of such cooperativity in a synthetic system is very challenging. In this work, we report the synthesis and characterization of diverse cesium coordination polymeric networks, a 2D sheet, [Cs{κ2‐C4H3N‐2(CH=N(2,6‐iPr2C6H3))}]∞ (1), an infinite ladder chain, [Cs{κ3‐C4H3N‐2(CH=N(CHPh2)}]∞ (2), and a 3D cage [Cs2{κ3‐(C4H3N‐2(CH=NCH2CH2N(CH2CH2)2O))}]∞ (3). This was accomplished by a variation of the substituent on the iminopyrrolyl ligand. Moreover, we studied a breakdown of the 2D sheet coordination polymer (1) into contact pair monomeric cesium complex [(18‐Cr‐6)Cs(κ2‐C4H3N‐2(CH=N(2,6‐iPr2C6H3)] (4). In the coordination polymers, multiple Cs centers are held together within a proximal distance (3.5–6 Å) via an iminopyrrolyl ligand. The Cs coordination polymer 2 displays the highest catalytic efficiency in rac‐lactide ROP, giving a TOF value of 49.5 h‐1 compared to 15.7 h‐1 and 24.0 h‐1 for 1 and 3. The higher reactivity of 2 is due to the metal‐metal cooperativity in cesium coordination polymeric networks.
Momentive’s SILTRUST™ RTV6561-LV and RTV6562-LV adhesives build on SILTRUST RTV566’s heritage in the optics industry. SILTRUST RTV566, a low outgassing, two-part, condensation cure silicone adhesive, maintains properties from -115°C to 260°C and typically reaches full properties after curing for seven days. SILTRUST RTV6561-LV and SILTRUST RTV6562-LV are heat-curable in contrast and are excellent candidate adhesives to consider for bonding sensory equipment. Similar to SILTRUST RTV566, they maintain properties at extreme high (260 °C) and low (-115 °C) temperatures and are ideal for bonding materials with dissimilar coefficients of thermal expansion. These products meet the low outgassing requirements set by ASTM E595 for total mass loss of less than 1% and collected volatile condensable materials of less than 0.1%. These adhesives offer high flow and low flow formats enabling process options for optics and sensor manufacturing. SILTRUST RTV6561-LV is flowable, whereas SILTRUST RTV6562-LV is a semi-flowable paste. They exhibit high tensile strengths with flexibility. A requirement for these products was to help enable rapid assembly. These grades can be cured with full properties in one hour at 80°C or in two days at room temperature with adhesive strength comparable to SILTRUST RTV566. These new silicone adhesives, SILTRUST RTV6561-LV and SILTRUST RTV6562-LV, have been formulated to provide primerless adhesion to many materials and can enable time saving and greater consistency during assembly tasks. Material shrinkage upon cure has been reduced compared to the legacy product.
COVID-19 testing provides information regarding exposure and transmission risks, guides preventative measures (e.g., if and when to start and end isolation and quarantine), identifies opportunities for appropriate treatments, and helps assess disease prevalence (1). At-home rapid COVID-19 antigen tests (at-home tests) are a convenient and accessible alternative to laboratory-based diagnostic nucleic acid amplification tests (NAATs) for SARS-CoV-2, the virus that causes COVID-19 (2-4). With the emergence of the SARS-CoV-2 B.1.617.2 (Delta) and B.1.1.529 (Omicron) variants in 2021, demand for at-home tests increased† (5). At-home tests are commonly used for school- or employer-mandated testing and for confirmation of SARS-CoV-2 infection in a COVID-19-like illness or following exposure (6). Mandated COVID-19 reporting requirements omit at-home tests, and there are no standard processes for test takers or manufacturers to share results with appropriate health officials (2). Therefore, with increased COVID-19 at-home test use, laboratory-based reporting systems might increasingly underreport the actual incidence of infection. Data from a cross-sectional, nonprobability-based online survey (August 23, 2021-March 12, 2022) of U.S. adults aged ≥18 years were used to estimate self-reported at-home test use over time, and by demographic characteristics, geography, symptoms/syndromes, and reasons for testing. From the Delta-predominant period (August 23-December 11, 2021) to the Omicron-predominant period (December 19, 2021-March 12, 2022)§ (7), at-home test use among respondents with self-reported COVID-19-like illness¶ more than tripled from 5.7% to 20.1%. The two most commonly reported reasons for testing among persons who used an at-home test were COVID-19 exposure (39.4%) and COVID-19-like symptoms (28.9%). At-home test use differed by race (e.g., self-identified as White [5.9%] versus self-identified as Black [2.8%]), age (adults aged 30-39 years [6.4%] versus adults aged ≥75 years [3.6%]), household income (>$150,000 [9.5%] versus $50,000-$74,999 [4.7%]), education (postgraduate degree [8.4%] versus high school or less [3.5%]), and geography (New England division [9.6%] versus West South Central division [3.7%]). COVID-19 testing, including at-home tests, along with prevention measures, such as quarantine and isolation when warranted, wearing a well-fitted mask when recommended after a positive test or known exposure, and staying up to date with vaccination,** can help reduce the spread of COVID-19. Further, providing reliable and low-cost or free at-home test kits to underserved populations with otherwise limited access to COVID-19 testing could assist with continued prevention efforts.
When individuals evaluate policies, they consider both the policy’s content and its endorsers. In this study, we investigate the conditions under which these sometimes competing factors guide preferences. In an effort to combat the spread of COVID-19, American President Trump and Canadian Prime Minister Trudeau bilaterally agreed to close their shared border to refugee claimants and asylum seekers. These ideologically opposed leaders endorsing a common policy allows us to test the influence of a well-known foreign neighbor on domestic policy evaluations. With a large cross-national survey experiment, we first find that Canadians and Americans follow ideological positions in evaluating the policy, with right-leaning respondents offering the most support. With an experiment, we reveal how both populations shift their views when told about their neighboring leader’s endorsement. Our findings highlight ideologically motivated reasoning across an international border, with broad implications for understanding how individuals weigh a policy’s content against its political cues.
Momentive offers solutions in market research, customer experience, and enterprise feedback. The technology is gleaned from the billions of real responses to questions asked on the platform. However, people may create biased questions. A double-barreled question (DBQ) is a common type of biased question that asks two aspects in one question. For example, "Do you agree with the statement: The food is yummy, and the service is great.". This DBQ confuses survey respondents because there are two parts in a question. DBQs impact both the survey respondents and the survey owners. Momentive aims to detect DBQs and recommend survey creators to make a change towards gathering high quality unbiased survey data. Previous research work has suggested detecting DBQs by checking the existence of grammatical conjunction. While this is a simple rule-based approach, this method is error-prone because conjunctions can also exist in properly constructed questions. We present an end-to-end machine learning approach for DBQ classification in this work. We handled this imbalanced data using active learning, and compared state-of-the-art embedding algorithms to transform text data into vectors. Furthermore, we proposed a model interpretation technique propagating the vector-level SHAP values to a SHAP value for each word in the questions. We concluded that the word2vec subword embedding with maximum pooling is the optimal word embedding representation in terms of precision and running time in the offline experiments using the survey data at Momentive. The A/B test and production metrics indicate that this model brings a positive change to the business. To the best of our knowledge, this is the first machine learning framework for DBQ detection, and it successfully differentiates Momentive from the competitors. We hope our work sheds light on machine learning approaches for bias question detection.
