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ABSTRACT Biomimetic designs are inspired by the complex and unique behavior of naturally occurring materials, and can be applied to many systems, including polymers. ZIPer polymers (Zwitter arene‐ion like polymer) are inspired by byssal threads found on mussels, and their physical state is highly sensitive to various environmental conditions. Specifically, the ZIPer polymer undergoes chemospecific phase transitions, exhibiting potential for its use as an ionic responsive technology. Though this phenomenon has been observed with Raman spectroscopy, little is known about how salt identity or concentration affect polymer inter‐ and intra‐chain interactions. Previous studies have used Raman spectroscopy to analyze ZIPer polymer behavior in the presence of salt; however, the effect is typically only observed with sodium chloride and often only compares spectra at two concentrations. Additionally, studies have mainly focused on the spectral evidence of cation–π interactions, significantly narrowing their spectral range. In order to develop a more predictive framework for ZIPer polymer behavior, a range of salt identities and concentrations need to be tested. This study uses Raman spectroscopy to investigate ZIPer polymer behavior in the presence of a series of salts, namely NaCl, NaOTFA, NaBr, NaBF₄, and NaPF₆, each at 0.1 M, 0.5 M, 1.0 M, and 1.5 M concentrations. Moreover, we observe spectral changes in a range from 550 to 2000 cm⁻¹. Spectral evidence suggests that the cation–π interactions previously hypothesized to be the driver of ZIPer polymer behavior are not the only mechanism determining the chemoresponsive phase transitions. We hypothesize that cation–π interactions and dispersion forces are competing mechanisms controlling ZIPer polymer behavior. Furthermore, we suggest that at certain concentrations the dominating mechanism transitions, and this inflection point is salt identity dependent. 

Abstract An in-beam gamma-ray spectroscopy study of the even–even nucleus⁹²Mo has been carried out using the³⁰Si +⁶⁵Cu,¹⁸O +⁸⁰Se reactions at beam energies of 120 and 99 MeV, respectively. Angular distribution from the oriented state ratio (R_ADO) and linear polarization (Δ_asym) measurements have fixed most of the tentatively assigned spin-parity of the high-energy levels. A large-scale shell-model calculation using the GWBXG interaction has been carried out to understand the configuration and structure of both positive and negative parity states up to the highest observed spin. The high-spin states primarily originate from the coupling of excited proton- and neutron-core structures in an almost stretched manner. The systematics of the energy required to form a neutron particle-hole pair excitation,νg_9/2→νd_5/2, is discussed. The lifetimes of a few high-spin states have been measured using the Doppler shift attenuation method. Additionally, a qualitative argument is proposed to explain the comparatively strong E1 transition feeding the 7310.9 keV level. 

An important achievement in the field of causal inference was a complete characterization of when a causal effect, in a system modeled by a causal graph, can be determined uniquely from purely observational data. The identification algorithms resulting from this work produce exact symbolic expressions for causal effects, in terms of the observational probabilities. More recent work has looked at the numerical properties of these expressions, in particular using the classical notion of the condition number. In its classical interpretation, the condition number quantifies the sensitivity of the output values of the expressions to small numerical perturbations in the input observational probabilities. In the context of causal identification, the condition number has also been shown to be related to the effect of certain kinds of uncertainties in the structure of the causal graphical model. In this paper, we first give an upper bound on the condition number for the interesting case of causal graphical models with small “confounded components”. We then develop a tight characterization of the condition number of any given causal identification problem. Finally, we use our tight characterization to give a specific example where the condition number can be much lower than that obtained via generic bounds on the condition number, and to show that even “equivalent” expressions for causal identification can behave very differently with respect to their numerical stability properties. 

An important achievement in the field of causal inference was a complete characterization of when a causal effect, in a system modeled by a causal graph, can be determined uniquely from purely observational data. The identification algorithms resulting from this work produce exact symbolic expressions for causal effects, in terms of the observational probabilities. More recent work has looked at the numerical properties of these expressions, in particular using the classical notion of the condition number. In its classical interpretation, the condition number quantifies the sensitivity of the output values of the expressions to small numerical perturbations in the input observational probabilities. In the context of causal identification, the condition number has also been shown to be related to the effect of certain kinds of uncertainties in the structure of the causal graphical model. In this paper, we first give an upper bound on the condition number for the interesting case of causal graphical models with small “confounded components”. We then develop a tight characterization of the condition number of any given causal identification problem. Finally, we use our tight characterization to give a specific example where the condition number can be much lower than that obtained via generic bounds on the condition number, and to show that even “equivalent” expressions for causal identification can behave very differently with respect to their numerical stability properties.