Upper Layer-Modulated Pseudo Planar Heterojunction with Metal Complex Acceptor for Efficient and Stable Organic Photovoltaics.

Modulating self-aggregation and charge transport in the upper acceptor layer of the pseudo planar heterojunction (PPHJ) is crucial for enhancing dielectric constant and suppressing trap density, leading to efficient and stable organic photovoltaics (OPVs). In this work, a metal complex acceptor (MCA), PtAC-Cl, is selectively incorporated into the upper host Y6 layer of PPHJ to regulate morphology and fill trap states. There exists a strong chemical interaction between PtAC-Cl and Y6, which can promote electron transfer.

Physics-informed neural network for nonlinear dynamics of self-trapped necklace beams.

A physics-informed neural network (PINN) is used to produce a variety of self-trapped necklace solutions of the (2+1)-dimensional nonlinear Schrödinger/Gross-Pitaevskii equation. We elaborate the analysis for the existence and evolution of necklace patterns with integer, half-integer, and fractional reduced orbital angular momenta by means of PINN. The patterns exhibit phenomena similar to the rotation of rigid bodies and centrifugal force.

Controlled dispersive waves emission via swallowtail-type catastrophe pulses.

The dispersive waves (DWs) emission has emerged as an efficient way to extend the spectral range of a light source. However, the applications of the DWs are limited because of its narrowband and low power. It is crucial to find novel ways to manipulate the DWs radiation.

Numerical investigation of the fractional-soliton mode-locked fiber laser.

We propose and numerically investigate a fractional-soliton mode-locked fiber laser by utilizing an intracavity spectral pulse shaper (SPS). The fiber laser can generate stable fractional-soliton pulses for three different Lévy index α (1 < α < 2), whose profiles are all close to the sech shape. We find that the positions of Kelly sidebands, pulse energy, and peak power of the emitted fractional pulses conform to three theoretical expressions, respectively.

The fusion of vegetation indices increases the accuracy of cotton leaf area prediction.

Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.

Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected.

Spatiotemporal patterns, sustainability, and primary drivers of NDVI-derived vegetation dynamics (2003-2022) in Nepal.

Understanding the vegetation dynamics and their drivers in Nepal has significant scientific reference value for implementing sustainable ecological policies. This study provides a comprehensive analysis of the spatio-temporal variations in vegetation cover in Nepal from 2003 to 2022 using MODIS NDVI data and explores the effects of climatic factors and anthropogenic activities on vegetation. Mann-Kendall test was used to assess the significant trend in NDVI and was integrated with the Hurst exponent to predict future trends.

Electrical Control and Transport of Tightly Bound Interlayer Excitons in a MoSe_{2}/hBN/MoSe_{2} Heterostructure.

Controlling interlayer excitons in Van der Waals heterostructures holds promise for exploring Bose-Einstein condensates and developing novel optoelectronic applications, such as excitonic integrated circuits. Despite intensive studies, several key fundamental properties of interlayer excitons, such as their binding energies and interactions with charges, remain not well understood. Here we report the formation of momentum-direct interlayer excitons in a high-quality MoSe_{2}/hBN/MoSe_{2} heterostructure under an electric field, characterized by bright photoluminescence (PL) emission with high quantum yield and a narrow linewidth of less than 4 meV.

Pinecone derived hierarchical carbon nanostructure as a transducer in a solid-state ion-selective electrode for in vivo analysis of calcium ion.

Monitoring extracellular calcium ion (Ca) chemical signals in neurons is crucial for tracking physiological and pathological changes associated with brain diseases in live animals. Potentiometry based solid-state ion-selective electrodes (ISEs) with the assist of functional carbon nanomaterials as ideal solid-contact layer could realize the potential response for in vitro and in vivo analysis. Herein, we employ a kind of biomass derived porous carbon as a transducing layer to prompt efficient ion to electron transduction while stabilizes the potential drift.

Lateral Heterostructure Formed by Highly Thermally Conductive Fluorinated Graphene for Efficient Device Thermal Management.

The continued miniaturization of chips demands highly thermally conductive materials and effective thermal management strategies. Particularly, the high-field transport of the devices built with 2D materials is limited by self-heating. Here a systematic control of heat flow in single-side fluorinated graphene (FG) with varying degrees of fluorination is reported, revealing a superior room-temperature thermal conductivity as high as 128 W m K.

Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones.

Non-invasive assessment of periodontal inflammation by continuum-removal hemodynamic spectral indices.

Background: Hyperspectral techniques have aroused great interest in non-invasively measuring periodontal tissue hemodynamics. However, current studies mainly focused on three typical inflammation stages (healthy, gingivitis and periodontitis) and practical approaches for using optical spectroscopy for early and precisely detection of periodontal inflammation at finer disease stages have not been well studied.

Methods: This study provided novel spectroscopic insights into periodontitis at different stages of disease, and developed six simple but physically meaning hemodynamic spectral indices (HSIs) including four spectral absorption depths of oxyhemoglobin ( ), deoxyhemoglobin ( ), total hemoglobin ( ) and tissue water ( ), and two normalized difference indices of oxyhemoglobin( ) and deoxyhemoglobin ( ) from continuum-removal spectra (400-1700 nm) of periodontal tissue collected from 47 systemically healthy subjects over different severities from healthy, gingivitis, slight, moderate to severe periodontitis for early and precision diagnostics of periodontitis.

Soliton and dispersive wave generation with third-order dispersion and temporal boundary.

We investigate the pulse evolution and energy conservation condition at the temporal boundary under third-order dispersion. When the fundamental soliton crosses the temporal boundary and forms two reflected pulses and one transmitted pulse, the power of the transmitted pulse first increases and then decreases as the incident spectrum shifts toward the blue side. If the transmitted spectrum lies in the anomalous group-velocity dispersion region, second-order soliton is formed and dispersive wave is radiated.

The Effect of Comb Cell Size on the Development of Drones.

The growth and development of honeybees are influenced by many factors, one of which is the cell size of the brood comb. Larger worker bees can be obtained by being raised in bigger cells. However, whether cell size has the same effect on drone development is still unknown.

Identification of potential pathogenic hepatic super-enhancers regulatory network in high-fat diet induced hyperlipidemia.

Hyperlipidemia (HLP) is a prevalent metabolic disorder and a significant risk factor for cardiovascular disease. According to recent discoveries, super-enhancers (SEs) play a role in the increased expression of genes that encode important regulators of both cellular identity and the progression of diseases. However, the underlying function of SEs in the development of HLP is still unknown.

Dispersive wave manipulation by the spectral Heaviside step phase modulation.

We investigate the dispersive waves (DWs) emitted from shaped pulses with spectral Heaviside step phases (HSPs). The spectrally HSP-modulated pulse exhibits a unique double-peak structure, where the intensity and separation of the twin peaks are determined by the modulation depth and frequency detuning. By tailoring the parameters of the HSP suitably, we can control the DW emission with regard to resonant frequency and conversion efficiency.

Nonlinear evolution of modulational instability under the interaction of Kerr nonlinearity with pure higher, even-order dispersion.

We investigate the nonlinear evolutions of modulation instability (MI) under the interaction of Kerr nonlinearity with pure higher, even-order dispersion (HEOD) by using the truncating method of three-wave mixing. For any HEOD, we find the phase-plane topological structure of the MI changes in three frequency regions whose ranges depend on the order of HEOD. And we present the novel types of nonlinear evolutions of the MI, which do not exist in the case of quadratic dispersion.

Directly seeding epitaxial growth of tungsten oxides/tungsten diselenide mixed-dimensional heterostructures with excellent optical properties.

Mixed-dimensional heterostructures have drawn significant attention due to their intriguing physical properties and potential applications in electronic and optoelectronic nanodevices. However, limited by the lattice matching, the preparation of heterostructures is experimentally difficult and the underlying growth mechanism has not been well established. Here, we report a three-step seeding epitaxial growth strategy for synthesizing mixed-dimensional heterostructures of one-dimensional microwire (MW) and two-dimensional atomic thin film.

Achieving Desired Pseudo-Planar Heterojunction Organic Solar Cells via Binary-Dilution Strategy.

The sequential deposition process has demonstrated the great possibility to achieve a photolayer architecture with an ideal gradient phase separation morphology, which has a vital influence on the physical processes that determine the performance of organic solar cells (OSCs). However, the controllable preparation of pseudo-planar heterojunction (P-PHJ) with gradient distribution has not been effectively elucidated. Herein, a binary-dilution strategy is proposed, the PM6 solution with micro acceptor BO-4Cl and the L8-BO solution with micro donor PM6 respectively, to form P-PHJ film.

The 10-m cotton maps in Xinjiang, China during 2018-2021.

Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e.

Feature multi-level attention spatio-temporal graph residual network: A novel approach to ammonia nitrogen concentration prediction in water bodies by integrating external influences and spatio-temporal correlations.

Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water.

Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach.

In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence of multiple monitoring sites and their inherent connections with meteorological factors.