Advances in optoelectronics require materials with novel and engineered characteristics. A class of materials that has garnered tremendous interest is metal-halide perovskites, stimulated by meteoric increases in photovoltaic efficiencies of perovskite solar cells. In addition, recent advances have applied perovskite nanocrystals (NCs) in light-emitting devices. It was found recently that, for cesium lead-halide perovskite NCs, their unusually efficient light emission may be due to a unique excitonic fine structure composed of three bright triplet states that minimally interact with a proximal dark singlet state. To study this fine structure without isolating single NCs, we use multidimensional coherent spectroscopy at cryogenic temperatures to reveal coherences involving triplet states of a CsPbI NC ensemble. Picosecond time scale dephasing times are measured for both triplet and inter-triplet coherences, from which we infer a unique exciton fine structure level ordering composed of a dark state energetically positioned within the bright triplet manifold.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775787PMC
http://dx.doi.org/10.1126/sciadv.abb3594DOI Listing

Publication Analysis

Top Keywords

fine structure
12
multidimensional coherent
8
coherent spectroscopy
8
cesium lead-halide
8
lead-halide perovskite
8
perovskite nanocrystals
8
bright triplet
8
triplet states
8
triplet
5
spectroscopy reveals
4

Similar Publications

Radical covalent organic frameworks (RCOFs) have demonstrated significant potential in redox catalysis and energy conversion applications. However, the synthesis of stable RCOFs with well-defined neutral carbon radical centers is challenging due to the inherent radical instability, limited synthetic methods and characterization difficulties. Building upon the understanding of stable carbon radicals and structural modulations for preparing crystalline COFs, herein we report the synthesis of a crystalline carbon-centered RCOF through a facile post-oxidation process.

View Article and Find Full Text PDF

Atomically Dispersed FeMo Dual Sites for Enhanced Electrocatalytic Nitrogen Reduction.

ACS Appl Mater Interfaces

January 2025

State Key Laboratory of Fine Chemicals, Research and Development Center of Membrane Science and Technology, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.

The electrocatalytic nitrogen reduction reaction (eNRR) is an attractive strategy for the green and distributed production of ammonia (NH); however, it suffers from weak N adsorption and a high energy barrier of hydrogenation. Atomically dispersed metal dual-site catalysts with an optimized electronic structure and exceptional catalytic activity are expected to be competent for knotty hydrogenation reactions including the eNRR. Inspired by the bimetallic FeMo cofactor in biological nitrogenase, herein, an atomically dispersed FeMo dual site anchored in nitrogen-doped carbon is proposed to induce a favorable electronic structure and binding energy.

View Article and Find Full Text PDF

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks.

View Article and Find Full Text PDF

Table Extraction with Table Data Using VGG-19 Deep Learning Model.

Sensors (Basel)

January 2025

Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.

In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for the precise identification and extraction of rows and columns from document images that contain tables.

View Article and Find Full Text PDF

WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models.

Sensors (Basel)

December 2024

Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.

Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!