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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Prediction of solar energetic events impacting space weather conditions

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Advances in Space Research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

arXiv

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05401 United States Vermont Advanced Computing Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States MassMutual Data Science AmherstMA01002 United States Howard Hughes Medical Institute University of California Berkeley BerkeleyCA94720 United States Data Visualization Lab Khoury College of Computer Sciences Northeastern University BostonMA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

Real-world complex systems often comprise many distinct types of elements as well as many more types of networked interactions between elements. When the relative abundances of types can be measured well, we further observe heavy-tailed categorical distributions for type frequencies. For the comparison of type frequency distributions of two systems or a system with itself at different time points in time—a facet of allotaxonometry—a great range of probability divergences are available. Here, we introduce and explore ‘probability-turbulence divergence’, a tunable, straightforward, and interpretable instrument for comparing normalizable categorical frequency distributions. We model probability-turbulence divergence (PTD) after rank-turbulence divergence (RTD). While probability-turbulence divergence is more limited in application than rank-turbulence divergence, it is more sensitive to changes in type frequency. We build allotaxonographs to display probability turbulence, incorporating a way to visually accommodate zero probabilities for ‘exclusive types’ which are types that appear in only one system. We explore comparisons of example distributions taken from literature, social media, and ecology. We show how probability-turbulence divergence either explicitly or functionally generalizes many existing kinds of distances and measures, including, as special cases, L(p) norms, the Sørensen-Dice coefficient (the F1 statistic), and the Hellinger distance. We discuss similarities with the generalized entropies of Rényi and Tsallis, and the diversity indices (or Hill numbers) from ecology. We close with thoughts on open problems concerning the optimization of the tuning of rank- and probability-turbulence divergence. Copyright © 2020, The Authors. All rights reserved.

关键词： Normal distribution

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Lorentzian geometry and variability reduction in airplane boarding: Slow passengers first outperforms random boarding

arXiv

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arXiv 2020年

作者： Erland, Sveinung Kaupužs, Jevgenijs Steiner, Albert Bachmat, Eitan Department of Maritime Studies Western Norway University of Applied Sciences HaugesundN-5528 Norway Faculty of Materials Science and Applied Chemistry Institute of Technical Physics Riga Technical University RigaLV-1048 Latvia Institute of Mathematical Sciences and Information Technologies University of Liepaja LiepajaLV-3401 Latvia Institute of Data Analysis and Process Design Zurich University of Applied Sciences ZHAW Winterthur8401 Switzerland Department of Computer Science Ben-Gurion University Beer-Sheva84105 Israel

Airlines use different boarding policies to organize the queue of passengers waiting to enter the airplane. We analyze three policies in the many-passenger limit by a geometric representation of the queue position and row designation of each passenger, and apply a Lorentzian metric to calculate the total boarding time. The boarding time is governed by the time each passenger needs to clear the aisle, and the added time is determined by the aisle-clearing time distribution through an effective aisle-clearing time parameter. The non-organized queues under the common random boarding policy are characterized by large effective aisle-clearing time. We show that, subject to a mathematical assumption which we have verified by extensive numerical computations in all realistic cases, the average total boarding time is always reduced when slow passengers are separated from faster passengers and the slow group is allowed to enter the airplane first. This is a universal result that holds for any combination of the three main governing parameters: the ratio between effective aisle-clearing times of the fast and the slow group, the fraction of slow passenger, and the congestion of passengers in the aisle. Separation into groups based on aisle-clearing time allows for more synchronized seating, but the result is non-trivial, as the similar fast-first policy — where the two groups enter the airplane in reverse order — is inferior to random boarding for a range of parameter settings. The asymptotic results conform well with discrete-event simulations with realistic number of passengers, and both the slow-first and the fast-first policies have the ability to perform unboundedly better than random boarding. Parameters based on empirical data, with hand luggage as criteria for separating passengers into the slow group, give a 13% reduction in total boarding time for slow first compared to random boarding. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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High-Performance Thermoelectric α-Ag9Ga1−xTe6Compounds with Ultralow Lattice Thermal Conductivity Originating from Ag9Te2Motifs

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Angewandte Chemie 2022年第36期134卷

作者： Yingfei Tang Yimeng Yu Na Zhao Keke Liu Haijie Chen Constantinos C. Stoumpos Yixuan Shi Shuo Chen Lingxiao Yu Jinsong Wu Qingjie Zhang Xianli Su Xinfeng Tang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 China International School of Materials Science and Engineering Wuhan University of Technology Wuhan 430070 China Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Investigation (equal) Methodology (equal) Visualization (equal) Writing - original draft (lead) Nanostructure Research Center Wuhan University of Technology Wuhan 430070 China Contribution: Data curation (equal) Software (equal) Visualization (supporting) Contribution: Investigation (equal) Methodology (equal) Contribution: Investigation (equal) Methodology (equal) Visualization (equal) State Key Laboratory for Modification of Chemical Fibers and Polymer Materials Institute of Functional Materials College of Materials Science and Engineering Donghua University Shanghai 201620 China Contribution: Methodology (equal) Visualization (equal) Writing - review & editing (equal) Department of Materials Science and Technology Voutes Campus University of Crete Iraklio Heraklion 70013 Greece Contribution: Methodology (equal) Visualization (equal) Contribution: Investigation (equal) Visualization (equal) Contribution: Conceptualization (equal) Investigation (equal) Contribution: Methodology (equal) Contribution: Conceptualization (equal) Methodology (equal) Contribution: Conceptualization (equal) Investigation (equal) Methodology (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Funding acquisition (lead) Methodology (equal) Supervision (equal) Writing - review & editing (equal)

We have determined the complex atomic structure of high-temperature α-Ag 9 GaTe 6 phase with a hexagonal lattice ( P 6 3 mc space group, a = b =8.2766 Å, c =13.4349 Å). The structure has outer [GaTe 4 ] 5− tetrahedrons and inner [Ag 9 Te 2 ] 5+ clusters. All of the Ag ions are disorderly distributed in the lattice. Seven types of the Ag atoms constitute the cage-like [Ag 9 Te 2 ] 5+ clusters. The highly disordered Ag ions vibrate in-harmonically, producing strong coupling between low frequency optical phonons and acoustic phonons. This in conjunction with a low sound velocity of 1354 m s −1 leads to an ultralow thermal conductivity of 0.20 W m −1 K −1 at 673 K. Meanwhile, the deficiency of Ga in Ag 9 Ga 1− x Te 6 compounds effectively optimizes the electronic transport properties. Ag 9 Ga 0.91 Te 6 attains a highest power factor of 0.40 mW m −1 K −2 at 673 K. All these contribute to a much-improved ZT value of 1.13 at 623 K for Ag 9 Ga 0.95 Te 6 , which is 41 % higher than that of the pristine Ag 9 GaTe 6 sample.

关键词： α-Ag9GaTe6 Crystal Structure Ga Deficiency Thermoelectrics Ultralow Lattice Thermal Conductivity

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Evolutionary Algorithm Directed Synthesis of Mixed Anion Compounds LaF2X (X=Br, I) and LaFI2

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Angewandte Chemie 2023年第30期135卷

作者： Dr. Daichi Kato Dr. Peng Song Dr. Hiroki Ubukata Haruki Taguro Dr. Cédric Tassel Dr. Kohei Miyazaki Prof. Takeshi Abe Dr. Kousuke Nakano Prof. Kenta Hongo Prof. Ryo Maezono Prof. Hiroshi Kageyama Department of Energy and Hydrocarbon Chemistry Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan PRESTO Japan Science and Technology Agency (JST) Kawaguchi Saitama 332-0012 Japan Contribution: Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Investigation (lead) Methodology (lead) Project administration (lead) Resources (supporting) Supervision (lead) Validation (equal) Visualization (equal) Writing - original draft (lead) Writing - review & editing (lead) School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Contribution: Data curation (supporting) Formal analysis (supporting) Funding acquisition (supporting) Methodology (supporting) Software (supporting) Validation (supporting) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Writing - original draft (supporting) Contribution: Funding acquisition (supporting) Resources (supporting) Writing - original draft (supporting) Center for Basic Research on Materials (CBRM) National Institute for Materials Science (NIMS) 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan Contribution: Writing - original draft (supporting) Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Contribution: Conceptualization (lead) Funding acquisition (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - original draft (supporting)

Mixed-anion compounds have attracted growing attentions, but their synthesis is challenging, making a rational search desirable. Here, we explored LaF 3 -LaX 3 (X=Cl, Br, I) system using ab initio structure searches based on evolutionary algorithms, and predicted LaF 2 X and LaFX 2 (X=Br, I), which are respectively isostructural with LaHBr 2 and YH 2 I, consisting of layered La-F blocks with single and double ordered honeycomb lattices, separated by van der Waals gaps. We successfully synthesized these compounds: LaF 2 Br and LaFI 2 crystallize in the predicted structure, while LaF 2 I is similar to the predicted one but with different layer stacking. LaF 2 I exhibits fluoride ion conductivity comparable to that of non-doped LaF 3 , and has the potential to show better ionic conductivity upon appropriate doping, given the theoretically lower diffusion energy barrier and the presence of soft iodine anions. This study shows the structure prediction using evolutionary algorithms will accelerate the discovery of mixed-anion compounds in future, in particular those with an ordered anion arrangement.

关键词： Evolutionary Algorithm Fluoride Ion Conductor Mixed Anion

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Carbazole-Dendronized Luminescent Radicals

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Angewandte Chemie 2023年第28期135卷

作者： Rui Xiaotian Wataru Ota Prof. Tohru Sato Minori Furukori Prof. Yasuo Nakayama Prof. Takuya Hosokai Eri Hisamura Kazuhiro Nakamura Kenshiro Matsuda Dr. Kohei Nakao Prof. Andrew P. Monkman Prof. Ken Albrecht Department of Applied Science for Electronics and Materials Interdisciplinary Graduate School of Engineering Sciences Kyushu University 6-1 Kasuga-Koen Kasuga-shi Fukuoka 816-8580 Japan Contribution: Conceptualization (supporting) Data curation (lead) Formal analysis (lead) Funding acquisition (supporting) Investigation (equal) Visualization (equal) Writing - original draft (lead) MOLFEX Inc. Takano Nishibiraki-cho 34–4 Sakyo-ku Kyoto 606-8103 Japan Contribution: Conceptualization (supporting) Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Resources (supporting) Software (supporting) Visualization (supporting) Writing - original draft (supporting) Fukui Institute for Fundamental Chemistry Kyoto University Takano Nishibiraki-cho 34–4 Sakyo-ku Kyoto 606-8103 Japan Department of Molecular Engineering Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan Elements Strategy Initiative for Catalysts & Batteries (ESICB) Kyoto University Kyotodaigaku Katsura Nishikyo-ku Kyoto 615-8245 Japan Contribution: Conceptualization (supporting) Software (supporting) Supervision (equal) Validation (supporting) Writing - review & editing (supporting) Department of Pure and Applied Chemistry Graduate School of Science and Technology Tokyo University of Science 2641 Yamazaki Noda Chiba 278-8510 Japan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Central 5 1-1-1 Higashi Tsukuba Ibaraki 305-8565 Japan Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Contribution: Supervision (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (supporting) Funding acquisition (supporting) Supervision (equal) Writing - review & editing (equal) Institute for Materials Chemistry and Engineering Kyushu University 6-1 Kasuga-Koen Kasuga-shi Fukuoka 816-8580 J

A series of carbazole-dendronized tris(2,4,6-trichlorophenyl)methyl (TTM) radicals have been synthesized. The photophysical properties of dendronized radicals up to the fourth generation were compared systematically to understand how structure–property relationships evolve with generation. The photoluminescence quantum yield (PLQY) was found to increase with the increasing generation, and the fourth generation (G4TTM) in cyclohexane solution showed a PLQY as high as 63 % at a wavelength of 627 nm (in the deep-red region) from the doublet state. The dendron modification strategy also showed a blue-shift of the emission on increasing the generation number, and the photostability was also increased compared to the bare TTM radical.

关键词： Carbazoles Dendrimers Excited Doublet State Luminescencent Materials Stable Radicals

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Cations Determine the Mechanism and Selectivity of Alkaline Oxygen Reduction Reaction on Pt(111)**

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Angewandte Chemie 2023年第51期135卷

作者： Dr. Tomoaki Kumeda Dr. Laura Laverdure Prof. Dr. Karoliina Honkala Dr. Marko M. Melander Prof. Dr. Ken Sakaushi Research Center for Energy and Environmental Materials National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Contribution: Data curation (lead) Investigation (lead) Validation (lead) Visualization (lead) Writing - original draft (equal) Department of Chemistry Nanoscience Center University of Jyväskylä P.O. Box 35 40014 Jyväskylä Finland Contribution: Formal analysis (supporting) Investigation (supporting) Validation (supporting) Writing - review & editing (supporting) Contribution: Funding acquisition (lead) Investigation (supporting) Supervision (supporting) Validation (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (supporting) Data curation (equal) Formal analysis (equal) Funding acquisition (equal) Investigation (equal) Project administration (supporting) Resources (equal) Software (lead) Supervision (equal) Validation (lead) Writing - original draft (equal) Contribution: Conceptualization (lead) Data curation (equal) Formal analysis (equal) Funding acquisition (lead) Investigation (equal) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - original draft (equal) Writing - review & editing (lead)

The proton-coupled electron transfer (PCET) mechanism of the oxygen reduction reaction (ORR) is a long-standing enigma in electrocatalysis. Despite decades of research, the factors determining the microscopic mechanism of ORR-PCET as a function of pH, electrolyte, and electrode potential remain unresolved, even on the prototypical Pt(111) surface. Herein, we integrate advanced experiments, simulations, and theory to uncover the mechanism of the cation effects on alkaline ORR on well-defined Pt(111). We unveil a dual-cation effect where cations simultaneously determine i) the active electrode surface by controlling the formation of Pt−O and Pt−OH overlayers and ii) the competition between inner- and outer-sphere PCET steps. The cation-dependent transition from Pt−O to Pt−OH determines the ORR mechanism, activity, and selectivity. These findings provide direct evidence that the electrolyte affects the ORR mechanism and performance, with important consequences for the practical design of electrochemical systems and computational catalyst screening studies. Our work highlights the importance of complementary insight from experiments and simulations to understand how different components of the electrochemical interface contribute to electrocatalytic processes.

关键词： Constrained DFT Electrocatalysis Energy Conversion Kinetic Isotope Effects Marcus Theory

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Improving the performance of AgCdF 3 -based perovskite solar cells using machine learning-driven adjustment of active layer and charge transport materials with SCAPS-1D

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Inorganic Chemistry Communications 2025年 179卷

作者： Avijit Ghosh Al Shahriar Zishan Mahbuba Moumita Yedluri Anil Kumar Anup Kumar Roy Siful Islam Shahan Ahmed Hasanur Zaman Anonto Asma Tabassum Happy Md Shameem Ahsan H.A. Alrafai Abeer A. Hassan Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur 5400 Bangladesh Department of Electrical and Electronics Engineering American International University – Bangladesh 408/1 Kuratoli Road Kuril Dhaka 1229 Bangladesh Department of Mathematics Lamar University Beaumont TX 77705 USA Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Phillip M. Drayer Department of Electrical and Computer Engineering Lamar University TX USA Master of Science in Management Information Systems College of Business Lamar University TX USA Master of Arts in Social Research & Data Analysis Montclair State University NJ USA Department of Industrial and Systems Engineering Lamar University Beaumont TX 77710 USA Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study investigates the possibility of combining inorganic substances—which act as absorbers, HTLs) in order to produce stable, effective PSCs. A ZnSe ETL, HTLs (MoS 2 , MoTe 2 , and CuI), and an AgCdF 3 perovskite absorber, comprise the suggested architecture for this study. This study examines the impact of metal contacts, doping density, interface defect densities, absorber thickness, HTL and ETL layers, and defect density on the output of a solar device using the SCAPS-1D model. The investigation of how temperature impacts the performance of the proposed PSC is another important aspect of the study. By integrating the MoS 2 /MoTe 2 /CuI layers as an HTL with the studied structure of Al/FTO/ZnSe/AgCdF 3 /HTL/Ni, the V OC climbed to 0.834/0.910/0.969 V, the J SC to 42.122/42.862/42.885 mA cm −2 , the PCE to 30.31/34.05/36.06 %, and the FF to 86.21/87.23/86.69 %. A machine learning model was created to forecast the solar cells’ performance metrics. The model precisely evaluates the importance of each parameter by utilizing SHAP values, providing important insights into their contributions. With an accuracy rate of around 83.75 %, machine learning predicted the performance matrix of the optimal solar cell under investigation. An affordable AgCdF 3 solar cell may be developed as a result of this study’s significant findings and practical design.

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