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Advances in characteristics improvement of polymeric membranes/separators for zinc-air batteries
(2022)
Zinc-air batteries (ZABs) are gaining popularity for a wide range of applications due to their high energy density, excellent safety, and environmental friendliness. A membrane/separator is a critical component of ZABs, with substantial implications for battery performance and stability, particularly in the case of a battery in solid state format, which has captured increased attention in recent years. In this review, recent advances as well as insight into the architecture of polymeric membrane/separators for ZABs including porous polymer separators (PPSs), gel polymer electrolytes (GPEs), solid polymer electrolytes (SPEs) and anion exchange membranes (AEMs) are discussed. The paper puts forward strategies to enhance stability, ionic conductivity, ionic selectivity, electrolyte storage capacity and mechanical properties for each type of polymeric membrane. In addition, the remaining major obstacles as well as the most potential avenues for future research are examined in detail.
Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
Ionic guest in ionic host
(2022)
Ionosilica ionogels, i.e. composites consisting of an ionic liquid (IL) guest confined in an ionosilica host matrix, were synthesized via a non-hydrolytic sol-gel procedure from a tris-trialcoxysilylated amine precursor using the IL [BMIM]NTf2 as solvent. Various ionosilica ionogels were prepared starting from variable volumes of IL in the presence of formic acid. The resulting brittle and nearly colourless monoliths are composed of different amounts of IL guests confined in an ionosilica host as evidenced via thermogravimetric analysis, FT-IR, and C-13 CP-MAS solid-state NMR spectroscopy. In the following, we focused on confinement effects between the ionic host and guest. Special host-guest interactions between the IL guest and the ionosilica host were evidenced by H-1 solid-state NMR, Raman spectroscopy, and broadband dielectric spectroscopy (BDS) measurements. The three techniques indicate a strongly reduced ion mobility in the ionosilica ionogel composites containing small volume fractions of confined IL, compared to conventional silica-based ionogels. We conclude that the ionic ionosilica host stabilizes an IL layer on the host surface; this then results in a strongly reduced ion mobility compared to conventional silica hosts. The ion mobility progressively increases for systems containing higher volume fractions of IL and finally reaches the values observed in conventional silica based ionogels. These results therefore point towards strong interactions and confinement effects between the ionic host and the ionic guest on the ionosilica surface. Furthermore, this approach allows confining high volume fractions of IL into self-standing monoliths while preserving high ionic conductivity. These effects may be of interest in domains where IL phases must be anchored on solid supports to avoid leaching or IL spilling, e.g., in catalysis, in gas separation/sequestration devices or for the elaboration of solid electrolytes for (lithium-ion) batteries and supercapacitors.
We characterize finite-time thermodynamic processes of multidimensional quadratic overdamped systems.
Analytic expressions are provided for heat, work, and dissipation for any evolution of the system covariance matrix.
The Bures-Wasserstein metric between covariance matrices naturally emerges as the local quantifier of dissipation.
General principles of how to apply these geometric tools to identify optimal protocols are discussed.
Focusing on the relevant slow-driving limit, we show how these results can be used to analyze cases in which the experimental control over the system is partial.
Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.
Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.
We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home.
During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others’ posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making.
One for all, all for one
(2022)
We propose a conceptual model of acceptance of contact tracing apps based on the privacy calculus perspective. Moving beyond the duality of personal benefits and privacy risks, we theorize that users hold social considerations (i.e., social benefits and risks) that underlie their acceptance decisions. To test our propositions, we chose the context of COVID-19 contact tracing apps and conducted a qualitative pre-study and longitudinal quantitative main study with 589 participants from Germany and Switzerland. Our findings confirm the prominence of individual privacy calculus in explaining intention to use and actual behavior. While privacy risks are a significant determinant of intention to use, social risks (operationalized as fear of mass surveillance) have a notably stronger impact. Our mediation analysis suggests that social risks represent the underlying mechanism behind the observed negative link between individual privacy risks and contact tracing apps' acceptance. Furthermore, we find a substantial intention–behavior gap.