Digital camera sensors use color filters on photodiodes to achieve color selectivity. As the color filters and photosensitive silicon layers are separate elements, these sensors suffer from optical cross-talk, which sets limits to the minimum pixel size. Here, we report hybrid silicon-aluminum nanostructures in the extreme limit of zero distance between color filters and sensors. This design could essentially achieve submicrometer pixel dimensions and minimize the optical cross-talk arising from tilt illuminations. The designed hybrid silicon-aluminum nanostructure has dual functionalities. Crucially, it supports a hybrid Mie-plasmon resonance of magnetic dipole to achieve color-selective light absorption, generating electron hole pairs. Simultaneously, the silicon-aluminum interface forms a Schottky barrier for charge separation and photodetection. This design potentially replaces the traditional dye-based filters for camera sensors at ultrahigh pixel densities with advanced functionalities in sensing polarization and directionality, and UV selectivity via interband plasmons of silicon.
Rhenium disulfide belongs to group VII transition metal dichalcogenides (TMDs) with attractive properties such as exceptionally high refractive index and remarkable oscillator strength, large in-plane birefringence, and good chemical stability. Unlike most other TMDs, the peculiar optical properties of rhenium disulfide persist from bulk to the monolayer, making this material potentially suitable for applications in optical devices. In this work, we demonstrate with unprecedented clarity the strong coupling between cavity modes and excited states, which results in a strong polariton interaction, showing the interest of these materials as a solid-state counterpart of Rydberg atomic systems. Moreover, we definitively clarify the nature of important spectral features, shedding light on some controversial aspects or incomplete interpretations and demonstrating that their origin is due to the interesting combination of the very high refractive index and the large oscillator strength expressed by these TMDs.
Fluvial carbon fluxes (FCFs) have attracted growing attention in recent decades due to its indispensable role in the global carbon cycle and budgets. To identify the major characteristics and evolutionary trends of FCFs related research, this study adopts a bibliometric method to analyze the publications retrieved from the database of Web of Science during 1997-2022. The information related to countries, institutes, authors, journals, collaboration, keywords and research trends is presented. Findings show that the publication number of FCFs related research had significant increase in the past 25 years. Science of the Total Environment, Biogeosciences and Journal of Hydrology were the most influential journals in this field. China, the USA and France ranked the top 3 countries in publication number. Previous studies concentrated on the source and fate, influential factors, process and estimation model of FCFs. The research trend of FCFs may focus on FCFs of Arctic rivers and their biogeochemical processes, the impact of human activities on FCFs, new techniques developed for FCFs research, and carbon exchange estimates across water air interface. This study provides researchers with a better understanding of the current state of FCFs and serves as an effective reference for future studies.
Forensic Science International, Volume 341, 2022 Dec, Article 111467 | Kranenburg, Ruben F.; Ramaker, Henk Jan; van Asten, Arian C. Rapid and efficient identification of the precise isomeric form of new psychoactive substances (NPS) by forensic casework laboratories is a relevant challenge in the forensic field. Differences in legal status occur for ring isomeric speci...
International Journal of Computational Method , Ahead of Print. Artificial neural network (NN) has become one of the most widely used machine learning (ML) models for problems in science and engineering, including the fast developing artificial intelligence (AI) technology. In training an NN model for a problem, one of the most frequently asked questions is how many neurons or layers of neurons should be used for a given dataset with a number of samples (or data points). This paper provides an answer to this critical question, by presenting a Neurons Samples Theorem, which states, in short, that the number of neurons should be equal or less than the number of samples used to train the NN.