As one of next-generation semiconductors, hybrid halide perovskites with tailorable optoelectronic properties are promising for photovoltaics, lighting, and displaying. This tunability lies on variable crystal structures, wherein the spatial arrangement of halide octahedra is essential to determine the assembly behavior and materials properties. Herein, we report to manipulate their assembling behavior and crystal dimensionality by locally collective hydrogen bonding effects. Specifically, a unique urea-amide cation is employed to form corrugated 1D crystals by interacting with bromide atoms in lead octahedra via multiple hydrogen bonds. Further tuning the stoichiometry, cations are bonded with water molecules to create a larger spacer that isolates individual lead bromide octahedra. It leads to zero-dimension (0D) single crystals, which exhibit broadband 'warm' white emission with photoluminescence quantum efficiency 5 times higher than 1D counterpart. This work suggests a feasible strategy to modulate the connectivity of octahedra and consequent crystal dimensionality for the enhancement of their optoelectronic properties.
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http://dx.doi.org/10.1038/s41467-019-13264-5 | DOI Listing |
R Soc Open Sci
January 2025
Department of Biology, University of Konstanz, Konstanz, Germany.
Whether individuals exhibit consistent behavioural variation is a central question in the field of animal behaviour. This question is particularly interesting in the case of social animals, as their behaviour may be strongly modulated by the collective. In this study, we ask whether honeybees exhibit individual differences in stinging behaviour.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Department of Computer Science & Gonda Brain Science Center & BINA Nano-Technology Center Bar Ilan University, Bar Ilan University, Israel.
The emergence of collective order in swarms from local, myopic interactions of their individual members is of interest to biology, sociology, psychology, computer science, robotics, physics and economics. , whose members unknowingly work towards a common goal, are particularly perplexing: members sometimes take individual actions that maximize collective utility, at the expense of their own. This seems to contradict expectations of individual rationality.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Sorbonne Universite, CNRS, ISIR, Paris F-75005, France.
This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models.
View Article and Find Full Text PDFUnderstanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models.
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