Predicting Two-Photon Absorption Spectra of Octupolar Molecules: A Deep-Learning Approach Based Exclusively on Molecular Structures.

Octupolar molecules possessing a strong two-photon response are vital for numerous advanced applications. However, accurately predicting their two-photon absorption (TPA) spectra requires high-precision quantum chemical calculations, which are computationally expensive due to repeated simulations of molecular excited-state properties. To address this challenge, we introduce a deep learning approach capable of rapidly and accurately forecasting TPA spectra for octupolar molecules. By leveraging the geometric structure as an initial descriptor, we employ a graph neural network to predict the maximum two-photon transition wavelength and cross-section. Our model demonstrates a mean absolute percentage error of less than 4% compared to time-dependent density-functional theory calculations, effectively reproducing experimental observations. Notably, this deep learning technique is nearly 100 000 times faster than comparable quantum calculations, making it an efficient and cost-effective tool for simulating TPA properties of octupolar molecules. Furthermore, this method holds great promise for the high-throughput screening of exceptional TPA materials.

Revealing the influence of doping elements (Ge, Sn, Ca, and Sr) on the properties of α-CsPbI3: a DFT investigation.

In this study, using density functional theory, we calculated the band structure and photoelectric properties in a series of 12.5% B-doped (B = Ge, Sn, Ca, and Sr) CsPbI3 perovskite systems. It is found that Ge doping can improve the structural stability and is more conducive to applications under high-pressure or by applying stress via calculating the bond length, formation energy, elastic properties, and electronic local function. In addition, the optimal direction for applying stress is achieved according to the elastic properties. Furthermore, in terms of electronic properties, the reason of energy band variation and the influence of chemical bond on the structural stability of doped α-CsPbI3 are investigated. The possibility of the applications of the CsPb0.875B0.125I3 perovskite is explored based on the optical properties. Thus, the theoretical study of the CsPb0.875B0.125I3 perovskite provides novel insights into the design of next-generation photoelectric and photovoltaic materials.

Theoretical Insights on the Sensing Performance for Newly-synthesized Two-photon Fluorescent N2H4 Probes Based on Spirobifluorence.

The development of fluorescent probe for hydrazine (N2H4) detection has attracted much attention due to the important role of N2H4 plays in the fields of medicine, agriculture, biology and environments. In this paper, the optical properties and water solubility of two novel two-photon fluorescent molecular probes (Probe1 and Probe2) before and after the reaction with N2H4 are studied by using the density function theory. The results show that electronic distribution and transition dipole moment of the probes are obviously changed after the reaction with N2H4, thus the optical properties of the molecules are influenced and the detection of N2H4 are realized. In addition, photoinduced electron transfer processes for Probe1 and Probe2 in the presence of N2H4 are theoretically characterized, which explains the experimental observations from the microscopic mechanism. Special attention has been paid on the analysis of the two-photon absorption for the probes with the absence and presence of N2H4 by the response theory method. Both probes with good water solubility show large variation on the two-photon absorption cross section when reacts with N2H4. In particular, the two-photon absorption response of Probe2 is more obvious, so it possesses preferable two-photon fluorescence microscopic imaging ability. More importantly, the receptor effect on the sensing performances of the probes are demonstrated, providing a theoretical reference for the design and synthesis on more efficient two-photon fluorescence N2H4 probes. Our study provides necessary information on the response mechanism of the studied chemosensors and helps to establish the relationship between the structure and optical properties of two-photon fluorescence N2H4 probes.

Influence of Sr doping on the photoelectronic properties of CsPbX3 (X = Cl, Br, or I): a DFT investigation.

To broaden the application of cesium lead halide perovskites, doping technology has been widely proposed. In this study, we calculated a 12.5% concentration of a Sr-doped CsPbX3 (X = Cl, Br, or I) perovskite via density functional theory. The results showed that the bandgap energy of the perovskite increased by 0.2-0.3 eV. The high symmetry points of the energy band changed from R to Γ after Sr doping because the Sr doping affected the initial distribution of atomic orbital hybridization. In addition, optical absorption spectra after doping showed an obvious blueshift, whereas the absorption coefficient of CsPb0.875Sr0.125X3 had the same magnitude as undoped CsPbX3. Moreover, the effective masses of electrons and holes changed within a small range (0.01-0.03 m0) after Sr doping. According to the findings of this study, the CsPb0.875Sr0.125X3 perovskite is expected to become an ideal candidate material for designing photovoltaic and photoelectric devices.

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge-Cloud Computing.

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge-LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge-LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

Passively mode-locked red Pr:LiYF4 laser based on a two-dimensional palladium diselenide saturable absorber.

We report a passively mode-locked Pr:LiYF4 (Pr:YLF) visible laser using a palladium diselenide (PdSe2) as a saturable absorber (SA) for the first time, to the best of our knowledge. The nonlinear optical properties of two-dimensional (2D) PdSe2 nanosheets in the visible band were studied by the open-aperture Z-scan technique. The results indicate the significant saturable absorption properties of PdSe2 nanosheets in the visible region. Furthermore, the continuous wave mode-locked (CWML) visible laser based on PdSe2 SA was successfully realized. Ultrashort pulses as short as 35 ps were obtained at 639.5 nm with a repetition rate of 80.3 MHz and a maximum output power of 116 mW. The corresponding pulse energy was 1.44 nJ and peak power was 41.3 W. These results indicate that 2D PdSe2 SA is a promising high stability passively mode-locked device for ultrafast solid-state visible lasers.

Direct generation of continuous-wave and passively Q-switched visible vortex beams from a doughnut-shaped diode-pumped Pr: YLF laser.

We demonstrate the direct generation of visible vortex beams (LG01 mode) from a doughnut-shaped diode-pumped Pr:YLF laser. In continuous-wave mode, the maximum vortex output power was 36 mW at 523 nm, 354 mW at 607 nm, 838 mW at 639 nm, 722 mW at 721 nm, respectively. Moreover, based on this operation, the orange and red passively Q-switched vortex lasers were also achieved by inserting a Co:MgAl2O4 crystal into the laser cavity as a saturable absorber. The shortest pulse width of Q-switched vortex laser was 58 ns for 607 nm, and 34 ns for 639 nm, respectively. Our work provides a reliable and efficient method for the direct generation of visible vortex lasers for potential applications.

Strain-induced bandgap engineering in CsGeX3 (X = I, Br or Cl) perovskites: insights from first-principles calculations.

Based on density functional theory and following first-principles methods, this paper investigated the electronic structures, densities of states, effective masses of electrons and holes, and optical properties of CsGeX3 (X = I, Br or Cl) perovskites under triaxial strains of -4% to 4%. The calculated results show that the tuning range of the bandgaps of the CsGeI3, CsGeBr3, and CsGeCl3 perovskites are 1.16 eV, 1.64 eV, and 1.63 eV, respectively. This result shows that the bandgap of the CsGeX3 perovskite is tuned over the entire visible spectrum by applying strain. Also, it is found that the change of the bandgap is caused by the change of the Ge-X long bond. Besides, the optimal bandgaps of CsGeI3 and CsGeBr3 can be achieved by applying compressive strains, providing theoretical support for adjusting the bandgaps of CsGeX3 perovskites. The effective masses of electrons and holes of CsGeX3 perovskites decrease gradually with the strains changing from 4% to -4%, which is conducive to the transmission of electrons and holes. In addition, the optical properties of CsGeX3 perovskites change from redshifted to blueshifted under different strains.

Versatile Dicyanomethylene-Based Fluorescent Probes for the Detection of ß-Amyloid in Alzheimer's Disease: A Theoretical Perspective.

Motivated by the growing demand for target chemosensors designed with diagnostic or therapeutic capability for fibrils related to amyloidosis diseases, we investigated in the present work the response mechanism of dicyanomethylene-based fluorescent probes for amyloid fibril using a combined approach, including molecular docking, quantum mechanics/molecular mechanics (QM/MM), and the quantum chemical method. Various binding modes for the probes in ß-amyloid (Aß) are discussed, and the fibril environment-induced molecular optical changes at the most stable site are compared to the fibril-free situation in aqueous environments. The results reveal that the fluorescence enhancement for the probes in Aß observed experimentally is an average consequence over multiple binding sites. In particular, the conformational difference, including conjugation length and donor effect, significantly contributes to the optical property of the studied probes both in water and fibril. To further estimate the transition nature of the molecular photoabsorption and photoemission processes, the hole-electron distribution and the structural variation on the first excited state of the probes are investigated in detail. On the basis of the calculations, structure-property relationships for the studied chemosensors are established. Our computational approach with the ability to elucidate the available experimental results can be used for designing novel molecular probes with applications to Aß imaging and the early diagnosis of Alzheimer's disease.

The contact barrier of a 1T'/2H MoS2 heterophase bilayer and its modulation by adatom and strain: a first-principles study.

The successful synthesis of a 1T'/2H MoS2 heterophase bilayer offers potential building blocks for constructing novel nanoelectronic and optoelectronic devices. Here, first principles calculations are applied to explore and modulate its contact nature. The calculated results show a finite Schottky barrier of ∼0.56 eV, and a dominant tunneling barrier of ∼2 eV exists at the contact interface of the 1T'/2H MoS2 heterophase bilayer. The Schottky barrier can be eliminated by adatoms and strains. Although the two strategies have an insignificant effect on the dominant tunneling barrier, they alter the regions with local potentials lower than that of the inter-layer gap related barrier.

The grain boundary effect on mechanical and electronic transport properties of a striped borophene.

The grain boundary (GB) effect on the mechanical and electronic transport properties of a striped borophene are investigated based on first principles calculations. Three GBs, (1,2)|(1,2), (2,1)|(2,1) and (3,1)|(3,1), constructed using the translation vector method are verified to possess low formation energy and stability at room temperature. The presence of GBs does not destroy the metallic nature of borophene, but results in the accumulation of charge densities. The mechanical strength of borophene is decreased due to the introduction of GBs. Their fracture behaviors are more complex, accompanied by reconstructions in the GB region. The transport current is also degraded, which is mainly caused by GBs in the borophene giving rise to backscattering. The degree of these reductions rely on the specific structure of GBs.

Sensing Performance Investigations on Two-Photon Fluorescent Probes for Detecting ß-Amyloid in Alzheimer's Disease.

Alzheimer's disease (AD) is one of the most common forms of senile disease. In recent years, the incidence of AD has been increasing significantly with the acceleration of the aging process of the global population. However, current clinical drugs can only alleviate the symptoms of AD patients without healing the disease fundamentally. Therefore, it is of great significance to develop an effective small molecule diagnostic reagent for the early diagnosis of AD. In this paper, we employ an integrated approach, including molecular docking simulation and quantum mechanics/molecular mechanics calculation, to investigate the sensing performance of a series of donor-acceptor structural probes for the marker protein of AD (ß-amyloid). Results show that the probes display evident fluorescence enhancement when bound to the ß-amyloid, suggesting the effect of the environment on the molecular properties. Especially, the two-photon absorption cross-section of the probes increase drastically in the ß-amyloid compared to that in vacuum, which results from the larger electron delocalization and dipole moment in the fibrillary-like environment. Thus, one can propose that the studied probes are capable of application in two-photon fluorescent imaging, particularly those containing naphthalene rings as the donor or with a longer spacer group. Our calculations elucidate the experimental measurements reasonably, and further establish possible structure-property relationships that can be used to design novel biocompatible two-photon fluorescent probes for the diagnosis of Alzheimer's.

Theoretical Design of a Two-Photon Fluorescent Probe for Nitric Oxide with Enhanced Emission Induced by Photoninduced Electron Transfer.

In the present work, we systematically investigate the sensing abilities of two recently literature-reported two-photon fluorescent NO probes, i.e., the o-phenylenediamine derivative of Nile Red and the p-phenylenediamine derivative of coumarin. The recognition mechanisms of these probes are studied by using the molecular orbital classifying method, which demonstrates the photoinduced electron transfer process. In addition, we have designed two new probes by swapping receptor units present on fluorophores, i.e., the p-phenylenediamine derivative of Nile Red and the o-phenylenediamine derivative of coumarin. However, it illustrates that only the latter has ability to function as off-on typed fluorescent probe for NO. More importantly, calculations on the two-photon absorption properties of the probes demonstrate that both receptor derivatives of coumarin possess larger TPA cross-sections than Nile Red derivatives, which makes a better two photon fluorescent probe. Our theoretical investigations reveal that the underlying mechanism satisfactorily explain the experimental results, providing a theoretical basis on the structure-property relationships which is beneficial to developing new two-photon fluorescent probes for NO.

Theoretical Studies on Two-Photon Fluorescent Hg2+ Probes Based on the Coumarin-Rhodamine System.

The development of fluorescent sensors for Hg2+ has attracted much attention due to the well-known adverse effects of mercury on biological health. In the present work, the optical properties of two newly-synthesized Hg2+ chemosensors based on the coumarin-rhodamine system (named Pro1 and Pro2) were systematically investigated using time-dependent density functional theory. It is shown that Pro1 and Pro2 are effective ratiometric fluorescent Hg2+ probes, which recognize Hg2+ by Förster resonance energy transfer and through bond energy transfer mechanisms, respectively. To further understand the mechanisms of the two probes, we have developed an approach to predict the energy transfer rate between the donor and acceptor. Using this approach, it can be inferred that Pro1 has a six times higher energy transfer rate than Pro2. Thus the influence of spacer group between the donor and acceptor on the sensing performance of the probe is demonstrated. Specifically, two-photon absorption properties of these two probes are calculated. We have found that both probes show significant two-photon responses in the near-infrared light region. However, only the maximum two-photon absorption cross section of Pro1 is greatly enhanced with the presence of Hg2+, indicating that Pro1 can act as a potential two-photon excited fluorescent probe for Hg2+. The theoretical investigations would be helpful to build a relationship between the structure and the optical properties of the probes, providing information on the design of efficient two-photon fluorescent sensors that can be used for biological imaging of Hg2+ in vivo.

Time-frequency-space EEG decoding model based on dense graph convolutional network for stroke.

Stroke, a sudden cerebrovascular ailment resulting from brain tissue damage, has prompted the use of motor imagery (MI)-based Brain-Computer Interface (BCI) systems in stroke rehabilitation. However, analyzing EEG signals from stroke patients is challenging because of their low signal-to-noise ratio and high variability. Therefore, we propose a novel approach that combines the modified S-transform (MST) and a dense graph convolutional network (DenseGCN) algorithm to enhance the MI-BCI performance across time, frequency, and space domains. MST is a time-frequency analysis method that efficiently concentrates energy in EEG signals, while DenseGCN is a deep learning model that uses EEG feature maps from each layer as inputs for subsequent layers, facilitating feature reuse and hyper-parameters optimization. Our approach outperforms conventional networks, achieving a peak classification accuracy of 90.22% and an average information transfer rate (ITR) of 68.52 bits per minute. Moreover, we conduct an in-depth analysis of the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon in the deep-level EEG features of stroke patients. Our experimental results confirm the feasibility and efficacy of the proposed approach for MI-BCI rehabilitation systems.

EMPT: a sparsity Transformer for EEG-based motor imagery recognition.

Introduction: Transformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the self-attention mechanism to construct models on EEG data suffers from the problem of the large amount of data required and the complexity of the algorithm. Methods: We propose a Transformer neural network combined with the addition of Mixture of Experts (MoE) layer and ProbSparse Self-attention mechanism for decoding the time-frequency-spatial domain features from motor imagery (MI) EEG of spinal cord injury patients. The model is named as EEG MoE-Prob-Transformer (EMPT). The common spatial pattern and the modified s-transform method are employed for achieving the time-frequency-spatial features, which are used as feature embeddings to input the improved transformer neural network for feature reconstruction, and then rely on the expert model in the MoE layer for sparsity mapping, and finally output the results through the fully connected layer. Results: EMPT achieves an accuracy of 95.24% on the MI EEG dataset for patients with spinal cord injury. EMPT has also achieved excellent results in comparative experiments with other state-of-the-art methods. Discussion: The MoE layer and ProbSparse Self-attention inside the EMPT are subjected to visualisation experiments. The experiments prove that sparsity can be introduced to the Transformer neural network by introducing MoE and kullback-leibler divergence attention pooling mechanism, thereby enhancing its applicability on EEG datasets. A novel deep learning approach is presented for decoding EEG data based on MI.

Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network - Gradient Penalty Model.

Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.

A deep learning model for predicting selected organic molecular spectra.

Accurate and efficient molecular spectra simulations are crucial for substance discovery and structure identification. However, the conventional approach of relying on the quantum chemistry is cost intensive, which hampers efficiency. Here we develop DetaNet, a deep-learning model combining E(3)-equivariance group and self-attention mechanism to predict molecular spectra with improved efficiency and accuracy. By passing high-order geometric tensorial messages, DetaNet is able to generate a wide variety of molecular properties, including scalars, vectors, and second- and third-order tensors-all at the accuracy of quantum chemistry calculations. Based on this we developed generalized modules to predict four important types of molecular spectra, namely infrared, Raman, ultraviolet-visible, and 1H and 13C nuclear magnetic resonance, taking the QM9S dataset containing 130,000 molecular species as an example. By speeding up the prediction of molecular spectra at quantum chemical accuracy, DetaNet could help progress toward real-time structural identification using spectroscopic measurements.

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.

Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.