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1. Symmetric and Asymmetric Push-Pull Conjugates: Significance of Pull Group Strength on Charge Transfer and Separation

   https://doi.org/https://doi.org/10.1021/acs.jpcb.0c09996  

   Jang, Y; Rout, Y; Misra, R; D'Souza, F (February 2021, Journal of physical chemistry)

   The effect of acceptor strength on excited state charge‐transfer (CT) and charge separation (CS) in central phenothiazine (PTZ) derived symmetric 1 (PTZ-(TCBD-TPA)2) and asymmetric, 2 (PTZ-(TCBD/DCNQ-TPA)2) push-pull conjugates, in which triphenylamine (TPA) act as end capping and 1,1,4,4–tetracyanobuta–1,3–diene (TCBD) and cyclohexa–2,5–diene–1,4–ylidene–expanded TCBD (DCNQ) act as electron acceptor units is reported. Due to strong push-pull effects, intramolecular charge transfer (ICT) was observed in the ground state extending the absorption into the near-IR region. Electrochemical, spectroelectrochemical and computational studies coupled with energy level calculations predicted both 1 and 2 to be efficient candidates for ultrafast charge transfer. Subsequent femtosecond transient absorption studies along with global target analysis, performed in both polar and nonpolar solvents, confirmed such processes in which the CS was efficient in asymmetric 2 having both TCBD and DCNQ acceptors in polar benzonitrile while in toluene, only charge transfer was witnessed. This work highlights significance of number and strength of electron acceptor entities and the role of solvent polarity in multi-modular push-push systems to achieve ultrafast CS. 

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2. The pull of the MXene vortex

   https://doi.org/10.1038/s41565-022-01238-6  

   Moscatelli, A. (October 2022, Nature Nanotechnology)

   The term MXenes (pronounced ‘maxenes’) was first introduced in 2011 by Naguib et al. to indicate the exfoliated material obtained from a MAX bulk phase. A MAX phase (general formula Mx+1AXn, with n = 1–4) is a ternary carbide or nitride (X = C, N), with M an early transition metal and A an element from groups 13 or 14 of the periodic table. An important MAX phase material is Ti3AlC2 . To make the corresponding MXenes, Naguib et al. treated it with HF, which extracts the Al layer from the crystal structure, exposing the Ti atoms to possible chemical functionalization. In the first report, functionalization consisted of either F atoms or OH groups (after reacting with water). A sonication step then led to exfoliation into nanometer-thick crystal layers of the corresponding Ti3C2 MXene (general formula Mx+1Xn). 

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3. Implementing Push-Pull Efficiently in GraphBLAS

   https://doi.org/10.1145/3225058.3225122  

   Yang, Carl; Buluc, Aydin; Owens, John D. (August 2018, Proceedings of the International Conference on Parallel Processing)

   We factor Beamer's push-pull, also known as direction-optimized breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them for generalizability, asymptotic speedup, and contribution to overall speedup. We demonstrate that masking is critical for high performance and can be generalized to all graph algorithms where the sparsity pattern of the output is known a priori. We show that these graph algorithm optimizations, which together constitute DOBFS, can be neatly and separably described using linear algebra and can be expressed in the GraphBLAS linear-algebra-based framework. We provide experimental evidence that with these optimizations, a DOBFS expressed in a linear-algebra-based graph framework attains competitive performance with state-of-the-art graph frameworks on the GPU and on a multi-threaded CPU, achieving 101 GTEPS on a Scale 22 RMAT graph. 

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4. Antibody Pull-Down Experiments in Fission Yeast

   https://doi.org/10.1007/978-1-4939-7546-4_11  

   Dong, Q; Li, F. (January 2018, Methods in molecular biology)
5. Pull-back Geometry of Persistent Homology Encodings

   Liang, Shuang; Turkes, Renata; Li, Jiayi; Otter, Nina; Montufar, Guido (February 2024, Transactions on Machine Learning Research)

   Persistent homology (PH) is a method for generating topology-inspired representations of data. Empirical studies that investigate the properties of PH, such as its sensitivity to perturbations or ability to detect a feature of interest, commonly rely on training and testing an additional model on the basis of the PH representation. To gain more intrinsic insights about PH, independently of the choice of such a model, we propose a novel methodology based on the pull-back geometry that a PH encoding induces on the data manifold. The spectrum and eigenvectors of the induced metric help to identify the most and least significant information captured by PH. Furthermore, the pull-back norm of tangent vectors provides insights about the sensitivity of PH to a given perturbation, or its potential to detect a given feature of interest, and in turn its ability to solve a given classification or regression problem. Experimentally, the insights gained through our methodology align well with the existing knowledge about PH. Moreover, we show that the pull-back norm correlates with the performance on downstream tasks, and can therefore guide the choice of a suitable PH encoding. 

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