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This paper introduces an extensible framework to predict small-business closures to inform urban planners, lenders, and business owners as to factors to improve business resilience. This paper couples machine learning with two point of interest (POI) datasets and infrastructure data and uses New York State’s COVID-19 PAUSE as a stressor for investigating small-business resiliency. The study included 2537 food-related, non-chain, retail businesses across select New York City zip codes, of which 17.7% closed permanently. Macro-, meso-, and micro-levels of features included the neighborhood profile, street dynamics, and venue-specific, location-related characteristics. A Gaussian Mixture Neural Network model achieved 74.1% precision, 92.5% recall, and an 82.3% F1-score without use of financial data. High-end restaurants located further than average from public transit were most at risk for closure, while non-restaurant, food businesses in commercially diverse areas having higher-than-average social media ratings were least at risk. This paper introduces a model for timely prediction of pandemic-induced, food-related, small-business closures without reliance on private or protected financial data, and provides insights into urban design to promote small, food business survivability.

 

In recent decades, more than 100 different mechanophores with a broad range of activation forces have been developed. For various applications of mechanophores in polymer materials, it is crucial to selectively activate the mechanophores with high efficiency, avoiding nonspecific bond scission of the material. In this study, we embedded cyclobutane-based mechanophore cross-linkers (I and II) with varied activation forces (fa) in the first network of the double network hydrogels and quantitively investigated the activation selectivity and efficiency of these mechanophores. Our findings revealed that cross-linker I, with a lower activation force relative to the bonds in the polymer main chain (fa-I/fa-chain = 0.8 nN/3.4 nN), achieved efficient activation with 100% selectivity. Conversely, an increase of the activation force of mechanophore II (fa-II/fa-chain = 2.5 nN/3.4 nN) led to a significant decrease of its activation efficiency, accompanied by a substantial number of nonspecific bond scission events. Furthermore, with the coexistence of two cross-linkers, significantly different activation forces resulted in the almost complete suppression of the higher-force one (i.e., I and III, fa-I/fa-III = 0.8 nN/3.4 nN), while similar activation forces led to simultaneous activations with moderate efficiencies (i.e., I and IV, fa-I/fa-IV = 0.8 nN/1.6 nN). These findings provide insights into the prevention of nonspecific bond rupture during mechanophore activation and enhance our understanding of the damage mechanism within polymer networks when using mechanophores as detectors. Besides, it establishes a principle for combining different mechanophores to design multiple mechanoresponsive functional materials. 

Hydrogen fluoride (HF) is a versatile reagent for material transformation, with applications in self-immolative polymers, remodeled siloxanes, and degradable polymers. The responsive in situ generation of HF in materials therefore holds promise for new classes of adaptive material systems. Here, we report the mechanochemically coupled generation of HF from alkoxy-gem-difluorocyclopropane (gDFC) mechanophores derived from the addition of difluorocarbene to enol ethers. Production of HF involves an initial mechanochemically assisted rearrangement of gDFC mechanophore to α-fluoro allyl ether whose regiochemistry involves preferential migration of fluoride to the alkoxy-substituted carbon, and ab initio steered molecular dynamics simulations reproduce the observed selectivity and offer insights into the mechanism. When the alkoxy gDFC mechanophore is derived from poly(dihydrofuran), the α-fluoro allyl ether undergoes subsequent hydrolysis to generate 1 equiv of HF and cleave the polymer chain. The hydrolysis is accelerated via acid catalysis, leading to self-amplifying HF generation and concomitant polymer degradation. The mechanically generated HF can be used in combination with fluoride indicators to generate an optical response and to degrade polybutadiene with embedded HF-cleavable silyl ethers (11 mol %). The alkoxy-gDFC mechanophore thus provides a mechanically coupled mechanism of releasing HF for polymer remodeling pathways that complements previous thermally driven mechanisms.