Olfactory Dysfunction as a Marker for Cognitive Impairment in General Paresis of the Insane: A Clinical Study.

BACKGROUND General paresis of the insane (GPI) is characterized by cognitive impairment, neuropsychiatric symptoms, and brain structural abnormalities, mimicking many neuropsychiatric diseases. Olfactory dysfunction has been linked to cognitive decline and neuropsychiatric symptoms in numerous neuropsychiatric diseases. Nevertheless, it remains unclear whether patients with GPI experience olfactory dysfunction and whether olfactory dysfunction is associated with their clinical manifestations. MATERIAL AND METHODS Forty patients with GPI and 37 healthy controls (HCs) underwent the "Sniffin Sticks" test battery, Mini-Mental State Examination, and Neuropsychiatric Inventory to measure olfactory function, cognitive function, and neuropsychiatric symptoms, respectively. Brain structural abnormalities were evaluated using visual assessment scales including the medial temporal lobe atrophy (MTA) visual rating scale and Fazekas scale. RESULTS Compared with HCs, patients with GPI exhibited significant olfactory dysfunction, as indicated by deficits in the odor threshold (OT) (P=0.001), odor discrimination (OD) (P<0.001), and odor identification (OI) (P<0.001). In patients with GPI, the OI was positively correlated with cognitive function (r=0.57, P<0.001), but no significant correlation was found between olfactory function and neuropsychiatric symptoms, blood, or cerebrospinal fluid biomarkers (rapid plasma reagin circle card test and Treponema pallidum particle agglutination test), or brain structural abnormalities (MTA and Fazekas scale scores). Mediation analysis indicated that the impaired OI in patients with GPI was mediated by cognitive impairment and impaired OT respectively. CONCLUSIONS Patients with GPI exhibited overall olfactory dysfunction. OI is correlated with cognitive function and the impaired OI is mediated by cognitive impairment in patients with GPI. Thus, OI may serve as a marker for reflecting cognitive function in patients with GPI.

Lower Dorsal Lateral Prefrontal Cortex Functional Connectivity in Late-Life Depression With Suicidal Ideation.

OBJECTIVE: The dorsal lateral prefrontal cortex (DLPFC) has been identified as a neuromodulation target for alleviating suicidal ideation. Dysfunctional DLPFC has been implicated in suicidality in depression. This study aimed to investigate the functional connectivity (FC) of the DLPFC in late-life depression (LLD) with suicidal ideation. METHODS: Resting-state functional magnetic resonance imaging (fMRI) data from 32 LLD patients with suicidal ideation (LLD-S), 41 LLD patients without suicidal ideation (LLD-NS), and 54 healthy older adults (HOA) were analyzed using DLPFC seed-based FC analyses. Group differences in FC were examined, and machine learning was applied to explore the potential of DLPFC-FC for classifying LLD-S from LLD-NS. RESULTS: Abnormal DLPFC-FC patterns were observed in LLD-S, characterized by lower connectivity with the angular gyrus, precuneus, and superior frontal gyrus compared to LLD-NS and healthy controls. A classification model based on the identified DLPFC-FC achieved an accuracy of 75%. CONCLUSION: The lower FC of DLPFC networks may contribute to the neurobiological mechanism of suicidal ideation in late-life depression. These findings may facilitate suicide prevention for LLD by providing potential neuroimaging markers and network-based neuromodulation targets. However, further confirmation with larger sample sizes and experimental designs is warranted.

Universal A-Cation Splitting in LiNbO3-Type Structure Driven by Intrapositional Multivalent Coupling.

Understanding the electric dipole switching in multiferroic materials requires deep insight of the atomic-scale local structure evolution to reveal the ferroelectric mechanism, which remains unclear and lacks a solid experimental indicator in high-pressure prepared LiNbO3-type polar magnets. Here, we report the discovery of Zn-ion splitting in LiNbO3-type Zn2FeNbO6 established by multiple diffraction techniques. The coexistence of a high-temperature paraelectric-like phase in the polar Zn2FeNbO6 lattice motivated us to revisit other high-pressure prepared LiNbO3-type A2BB'O6 compounds. The A-site atomic splitting (∼1.0-1.2 Šbetween the split-atom pair) in B/B'-mixed Zn2FeTaO6 and O/N-mixed ZnTaO2N is verified by both powder X-ray diffraction structural refinements and high angle annular dark field scanning transmission electron microscopy images, but is absent in single-B-site ZnSnO3. Theoretical calculations are in good agreement with experimental results and suggest that this kind of A-site splitting also exists in the B-site mixed Mn-analogues, Mn2FeMO6 (M = Nb, Ta) and anion-mixed MnTaO2N, where the smaller A-site splitting (∼0.2 Šatomic displacement) is attributed to magnetic interactions and bonding between A and B cations. These findings reveal universal A-site splitting in LiNbO3-type structures with mixed multivalent B/B', or anionic sites, and the splitting-atomic displacement can be strongly suppressed by magnetic interactions and/or hybridization of valence bands between d electrons of the A- and B-site cations.

Orbital change manipulation metal-insulator transition temperature in W-doped VO2.

A series of epitaxial V1-xWxO2 (0 ≤ x ≤ 0.76%) nanocrystalline films on c-plane sapphire substrates have been successfully synthesized. Orbital structures of V1-xWxO2 films with monoclinic and rutile states have been investigated by ultraviolet-infrared spectroscopy combined with first principles calculations. Experimental and calculated results show that the overlap of π* and d∥ orbitals increases with increasing W doping content for the rutile state. Meanwhile, in the monoclinic state, the optical band gap decreases from 0.65 to 0.54 eV with increasing W doping concentration. Clear evidence is found that the V1-xWxO2 thin film phase transition temperature change comes from orbital structure variations. This shows that, with increasing W doping concentration, the decrease of rutile d∥ orbital occupancy can reduce the strength of V-V interactions, which finally results in phase transition temperature decrease. The experimental results reveal that the d∥ orbital is very important for the VO2 phase transition process. Our findings open a possibility to tune VO2 phase transition temperature through orbital engineering.

Simulation of a Multistaggered Baffle Scrubber to Enhance the Efficiency of Simultaneous Desulfurization and Denitrification.

In this study, we conduct simulation research on simultaneous desulfurization and denitrification in a multistaggered baffle spray scrubber. By employing two-phase flow simulations within the Euler-Lagrange framework and calculating the gas-liquid mass transfer rate with user-defined functions, we comprehensively analyzed the effects of various operational parameters. Initially, we validated our simulation model by comparing the simulation results with experimental data. Under conditions of a 0.2 mm droplet diameter, a liquid-to-gas ratio (L/G) of 12 L/m3, and a gas flow rate of 5 CMM using a full cone nozzle, the simulation indicated a desulfurization efficiency of 99.90 versus 99.84% obtained experimentally and a denitrification efficiency of 92.01 versus 90.67% obtained experimentally. This comparison confirmed the reliability of the simulation model. Our findings indicate that a droplet size of 2 mm is optimal, enhancing the desulfurization efficiency from 99.90 to 99.98% and the denitrification efficiency from 92.01 to 99.76%. However, when the droplet size exceeds 2 mm, efficiencies marginally decrease. Increasing the liquid-to-gas ratio to 16 L/m3 further improves desulfurization and denitrification efficiencies to 99.98 and 99.80%, respectively. In contrast, higher inlet flue gas flow rates reduce these efficiencies, with a decline observed from 100% to as low as 93.90% for denitrification with 2 mm droplets. Additionally, the use of a swirl cone nozzle, compared to full or hollow cone nozzles, better disperses droplets, enhancing the gas-liquid contact and achieving efficiencies of 99.99% for desulfurization and 99.81% for denitrification with 2 mm droplets. These insights are valuable for optimizing operational conditions in industrial-scale spray scrubbers, significantly contributing to mitigating the environmental impacts of industrial emissions.

Altered resting-state brain oscillation and the associated cognitive impairments in late-life depression with different depressive severity: An EEG power spectrum and functional connectivity study.

OBJECTIVE: Cognitive impairments are prevalent in late-life depression (LLD). However, it remains unclear whether there are concurrent brain oscillation alterations in resting condition across varying level of depression severity. This cross-sectional study aims to investigate the characteristics of altered resting-state oscillations, including power spectrum and functional connectivity, and their association with the cognitive impairments in LLD with different depression severity. METHODS: A total of 65 patients with LLD and 40 elder participants without depression were recruited. Global cognition and subtle cognitive domains were evaluated. A five-minute resting-state electroencephalography (EEG) was conducted under eyes-closed conditions. Measurements included the ln-transformed absolute power for power spectrum analysis and the weighted phase lag index (wPLI) for functional connectivity analysis. RESULTS: Attentional and executive dysfunction were exhibited in Moderate-Severe LLD group. Enhanced posterior upper gamma power was observed in both LLD groups. Additionally, enhanced parietal and fronto-parietal/occipital theta connectivity were observed in Moderate-Severe LLD group, which were associated with the attentional impairment. LIMITATIONS: Limitations include a small sample size, concomitant medication use, and a relatively higher proportion of females. CONCLUSIONS: Current study observed aberrant brain activity patterns in LLD across different levels of depression severity, which were linked to cognitive impairments. The altered posterior brain oscillations may be trait marker of LLD. Moreover, cognitive impairments and associated connectivity alterations were exhibited in moderate-severe group, which may be a state-like marker of moderate-to severe LLD. The study deepens understanding of cognitive impairments with the associated oscillation changes, carrying implications for neuromodulation targets in LLD.

Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression.

Background: Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear. Methods: Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed. Results: Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items. Conclusion: These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.

Observation of Strong Anisotropic Interlayer Excitons.

Anisotropic interlayer excitons had been theoretically predicted to exist in two-dimensional (2D) anisotropy/isotropy van der Waals heterojunctions. However, experimental results consolidating the theoretical prediction and exploring the related anisotropic optoelectronic response have not been reported so far. Herein, strong photoluminescence (PL) of anisotropic interlayer excitons is observed in a symmetric anisotropy/isotropy/anisotropy heterojunction exemplified by 3L-ReS2/1L-MoS2/3L-ReS2 using monolayer (1L) MoS2 and trilayer (3L) ReS2 as components. Sharp interlayer exciton PL peaks centered at ∼1.64, ∼1.61, and ∼1.57 eV are only observed at low temperatures of ≤120 K and become more pronounced as the temperature decreases. These interlayer excitons exhibit strong anisotropic PL intensity variations with periodicities of 180° as functions of the incident laser polarization angles. The polarization ratios of these interlayer excitons are calculated to be 1.33-1.45. Our study gives new insight into the manipulation of excitons in 2D materials and paves a new way for a rational design of novel anisotropic optoelectronic devices.

E 3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection.

Existing unsupervised outlier detection (OD) solutions face a grave challenge with surging visual data like images. Although deep neural networks (DNNs) prove successful for visual data, deep OD remains difficult due to OD's unsupervised nature. This paper proposes a novel framework named E 3Outlier that can perform effective and end-to-end deep outlier removal. Its core idea is to introduce self-supervision into deep OD. Specifically, our major solution is to adopt a discriminative learning paradigm that creates multiple pseudo classes from given unlabeled data by various data operations, which enables us to apply prevalent discriminative DNNs (e.g., ResNet) to the unsupervised OD problem. Then, with theoretical and empirical demonstration, we argue that inlier priority, a property that encourages DNN to prioritize inliers during self-supervised learning, makes it possible to perform end-to-end OD. Meanwhile, unlike frequently-used outlierness measures (e.g., density, proximity) in previous OD methods, we explore network uncertainty and validate it as a highly effective outlierness measure, while two practical score refinement strategies are also designed to improve OD performance. Finally, in addition to the discriminative learning paradigm above, we also explore the solutions that exploit other learning paradigms (i.e., generative learning and contrastive learning) to introduce self-supervision for E 3Outlier. Such extendibility not only brings further performance gain on relatively difficult datasets, but also enables E 3Outlier to be applied to other OD applications like video abnormal event detection. Extensive experiments demonstrate that E 3Outlier can considerably outperform state-of-the-art counterparts by 10%-30% AUROC. Demo codes are available at https://github.com/demonzyj56/E3Outlier.

Relationships Among Short Self-Reported Sleep Duration, Cognitive Impairment, and Insular Functional Connectivity in Late-Life Depression.

BACKGROUND: Both late-life depression (LLD) and short sleep duration increase the risk of cognitive impairment. Increased insular resting-state functional connectivity (FC) has been reported in individuals with short sleep duration and dementia. OBJECTIVE: This study aimed to investigate whether short sleep duration is associated with impaired cognition and higher insular FC in patients with LLD. METHODS: This case- control study recruited 186 patients with LLD and 83 normal controls (NC), and comprehensive psychometric assessments, sleep duration reports and resting-state functional MRI scans (81 LLD patients and 54 NC) were conducted. RESULTS: Patients with LLD and short sleep duration (LLD-SS patients) exhibited more severe depressive symptoms and worse cognitive function than those with normal sleep duration (LLD-NS patients) and NC. LLD-SS patients exhibited higher FC between the bilateral insula and inferior frontal gyrus (IFG) pars triangularis than LLD-NS patients and NC, while LLD-NS patients exhibited lower FC than NC. Increased insular FC was correlated with short sleep duration, severe depressive symptoms, and slower information processing speeds. Furthermore, an additive effect was found between sleep duration and LLD on global cognition and insular FC. CONCLUSION: LLD-SS patients exhibited impaired cognition and increased insular FC. Abnormal FC in LLD-SS patients may be a therapeutic target for neuromodulation to improve sleep and cognitive performance and thus decrease the risk of dementia.

Probing Nanoscale Exciton Funneling at Wrinkles of Twisted Bilayer MoS2 Using Tip-Enhanced Photoluminescence Microscopy.

In twisted bilayer (t2L) two-dimensional (2D) transition metal dichalcogenides, local strain at wrinkles strongly modulates the local exciton density and PL energy resulting in an exciton funneling effect. Probing such exciton behaviors especially at nanometer length scales is beyond the limit of conventional analytical tools due to the limited spatial resolution and low sensitivity. To address this challenge, herein we applied high-resolution tip-enhanced photoluminescence (TEPL) microscopy to investigate exciton funneling at a wrinkle in a t2L MoS2 sample with a small twist angle of 0.5°. Owing to a spatial resolution of <10 nm, excitonic behavior at nanoscale sized wrinkles could be visualized using TEPL imaging. Detailed investigation of nanoscale exciton funneling at the wrinkles revealed a deformation potential of -54 meV/%. The obtained results provide novel insights into the inhomogeneities of excitonic behaviors at nanoscale and would be helpful in facilitating the rational design of 2D material-based twistronic devices.

Label propagation via local geometry preserving for deep semi-supervised image recognition.

In this paper, we propose a novel transductive pseudo-labeling based method for deep semi-supervised image recognition. Inspired from the superiority of pseudo labels inferred by label propagation compared with those inferred from network, we argue that information flow from labeled data to unlabeled data should be kept noiseless and with minimum loss. Previous research works use scarce labeled data for feature learning and solely consider the relationship between two feature vectors to construct the similarity graph in feature space, which causes two problems that ultimately lead to noisy and incomplete information flow from labeled data to unlabeled data. The first problem is that the learned feature mapping is highly likely to be biased and can easily over-fit noise. The second problem is the loss of local geometry information in feature space during label propagation. Accordingly, we firstly propose to incorporate self-supervised learning into feature learning for cleaner information flow in feature space during subsequent label propagation. Secondly, we propose to use reconstruction concept to measure pairwise similarity in feature space, such that local geometry information can be preserved. Ablation study confirms synergistic effects from features learned with self-supervision and similarity graph with local geometry preserving. Extensive experiments conducted on benchmark datasets have verified the effectiveness of our proposed method.

Slice-Based Online Convolutional Dictionary Learning.

Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While yielding superior results compared to traditional sparse coding methods on various signal and image processing tasks, most CDL methods have difficulties handling large data, because they have to process all images in the dataset in a single pass. Therefore, recent research has focused on online CDL (OCDL) which updates the dictionary with sequentially incoming signals. In this article, a novel OCDL algorithm is proposed based on a local, slice-based representation of sparse codes. Such representation has been found useful in batch CDL problems, where the convolutional sparse coding and dictionary learning problem could be handled in a local way similar to traditional sparse coding problems, but it has never been explored under online scenarios before. We show, in this article, that the proposed algorithm is a natural extension of the traditional patch-based online dictionary learning algorithm, and the dictionary is updated in a similar memory efficient way too. On the other hand, it can be viewed as an improvement of existing second-order OCDL algorithms. Theoretical analysis shows that our algorithm converges and has lower time complexity than existing counterpart that yields exactly the same output. Extensive experiments are performed on various benchmarking datasets, which show that our algorithm outperforms state-of-the-art batch and OCDL algorithms in terms of reconstruction objectives.

Multimodal Nanoscopic Study of Atomic Diffusion and Related Localized Optoelectronic Response of WS2/MoS2 Lateral Heterojunctions.

The atomic diffusion in transition metal dichalcogenides (TMDs) van der Waals heterojunctions (HJs) strongly modifies their optoelectronic properties in the nanoscale. However, probing such localized properties challenges the spatial resolution and the sensitivity of a variety of analytic tools. Herein, a multimodal nanoscopy (based on tip enhanced Raman spectroscopy (TERS) and photoluminescence (TEPL)) combined with the Kelvin probe force microscopy (KPFM) method was used to probe such nanoscale localized optoelectronic properties induced by atomic diffusion. Chemical vapor deposition (CVD)-grown lateral bilayer (2L) WS2/MoS2 HJs were imaged with a spatial resolution better than 40 nm via TERS and TEPL mapping by using intrinsic Raman and photoluminescence (PL) peaks. The contact potential difference (CPD), capacitance, and PL variation in a nanoscale vicinity of the HJ interface can be correlated to the local stoichiometry variation determined by TERS. The diffusion coefficients of W and Mo were obtained to be ∼0.5 × 10-12 and ∼1 × 10-12 cm2/s, respectively, by using Fick's second law. The obtained results would be useful to further understand the localized optoelectronic response of the TMDs HJs.

End-to-end novel visual categories learning via auxiliary self-supervision.

Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data, which is impractical and restricted for real-world applications. In this research work, our focus is on semi-supervised learning when the categories of unlabeled data and labeled data are disjoint from each other. The main challenge is how to effectively leverage knowledge in labeled data to unlabeled data when they are independent from each other, and not belonging to the same categories. Previous state-of-the-art methods have proposed to construct pairwise similarity pseudo labels as supervising signals. However, two issues are commonly inherent in these methods: (1) All of previous methods are comprised of multiple training phases, which makes it difficult to train the model in an end-to-end fashion. (2) Strong dependence on the quality of pairwise similarity pseudo labels limits the performance as pseudo labels are vulnerable to noise and bias. Therefore, we propose to exploit the use of self-supervision as auxiliary task during model training such that labeled data and unlabeled data will share the same set of surrogate labels and overall supervising signals can have strong regularization. By doing so, all modules in the proposed algorithm can be trained simultaneously, which will boost the learning capability as end-to-end learning can be achieved. Moreover, we propose to utilize local structure information in feature space during pairwise pseudo label construction, as local properties are more robust to noise. Extensive experiments have been conducted on three frequently used visual datasets, i.e., CIFAR-10, CIFAR-100 and SVHN, in this paper. Experiment results have indicated the effectiveness of our proposed algorithm as we have achieved new state-of-the-art performance for novel visual categories learning for these three datasets.

ELM embedded discriminative dictionary learning for image classification.

Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. In many cases, the dataset size is often small with large intra-class variability and nondiscriminative feature space. In this work we propose a simple and effective framework called ELM-DDL to address these issues. Specifically, we represent input features with Extreme Learning Machine (ELM) with orthogonal output projection, which enables diverse representation on nonlinear hidden space and task specific feature learning on output space. The embeddings are further regularized via a maximum margin criterion (MMC) to maximize the inter-class variance and minimize intra-class variance. For dictionary learning, we design a novel weighted class specific ℓ1,2 norm to regularize the sparse coding vectors, which promotes uniformity of the sparse patterns of samples belonging to the same class and suppresses support overlaps of different classes. We show that such regularization is robust, discriminative and easy to optimize. The proposed method is combined with a sparse representation classifier (SRC) to evaluate on benchmark datasets. Results show that our approach achieves state-of-the-art performance compared to other dictionary learning methods.

Simultaneously learning affinity matrix and data representations for machine fault diagnosis.

Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data points in the original data space. The predefined affinity matrix, which is also known as the similarity matrix, is then used to preserve geometry information during the process of representations learning. Hence, the data representations are learned under the assumption of a fixed and known prior knowledge, i.e., similarities between data points. However, the assumed prior knowledge is difficult to precisely determine the real relationships between data points, especially in high dimensional space. Also, using two separated steps to learn affinity matrix and data representations may not be optimal and universal for data classification. In this paper, based on the extreme learning machine autoencoder (ELM-AE), we propose to learn the data representations and the affinity matrix simultaneously. The affinity matrix is treated as a variable and unified in the objective function of ELM-AE. Instead of predefining and fixing the affinity matrix, the proposed method adjusts the similarities by taking into account its capability of capturing the geometry information in both original data space and non-linearly mapped representation space. Meanwhile, the geometry information of original data can be preserved in the embedded representations with the help of the affinity matrix. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed method, and the empirical study also shows it is an efficient tool on machine fault diagnosis.

NbSeTe-a new layered transition metal dichalcogenide superconductor.

Transition metal dichalcogenides (TMDCs) usually exhibit layered polytypic structures due to the weak interlayer coupling. 2H-NbSe2 is one of the most widely studied in the pristine TMDC family due to its high superconducting transition temperature (T c = 7.3 K) and the occurrence of a charge-density wave (CDW) order below 33 K. The coexistence of CDW with superconductivity poses an intriguing open question about the relationship between Fermi surface nesting and Cooper pairing. Past studies of this issue have mostly been focused on doping 2H-NbSe2 by 3d transition metals without significantly changing its crystal structure. Here we replaced the Se by Te in 2H-NbSe2 in order to design a new 1T polytype layered TMDC NbSeTe, which adopts a trigonal structure with space group P [Formula: see text] m1. We successfully grew large size and high-quality single crystals of 1T-NbSeTe via the vapor transport method using I 2 as the transport agent. Temperature-dependent resistivity and specific heat data revealed a bulk T c at 1.3 K, which is the first observation of superconductivity in pure 1T-NbSeTe phase. This compound enlarged the family of superconducting TMDCs and provides an opportunity to study the interplay between CDW and superconductivity in the trigonal structure.

Topological phase transition induced by magnetic proximity effect in two dimensions.

We study the magnetic proximity effect on a two-dimensional topological insulator in a CrI3/SnI3/CrI3 trilayer structure. From first-principles calculations, the BiI3-type SnI3 monolayer without spin-orbit coupling has Dirac cones at the corners of the hexagonal Brillouin zone. With spin-orbit coupling turned on, it becomes a topological insulator, as revealed by a non-vanishing Z 2 invariant and an effective model from symmetry considerations. Without spin-orbit coupling, the Dirac points are protected if the CrI3 layers are stacked ferromagnetically, and are gapped if the CrI3 layers are stacked antiferromagnetically, which can be explained by the irreducible representations of the magnetic space groups [Formula: see text] and [Formula: see text], corresponding to ferromagnetic and antiferromagnetic stacking, respectively. By analyzing the effective model including the perturbations, we find that the competition between the magnetic proximity effect and spin-orbit coupling leads to a topological phase transition between a trivial insulator and a topological insulator.

The Structural and Electronic Properties of CdS/ZnS Core-Shell Nanowires.

The structural and electronic properties of the CdS/ZnS core-shell nanowires (NWs) oriented along [001] direction have been investigated by means of the first-principles calculation. It is found that CdS core suffers from the compressive strain in the CdS-core/ZnS-shell NWs, and ZnS core is stretched in the ZnS-core/CdS-shell NWs. A thicker ZnS shell can improve the NWs' stability, and a thicker CdS shell would decrease their stability. For both CdS/ZnS core-shell NWs, the band gap decreases linearly with increasing the shell when the core size is fixed. However, when the diameter of NWs is fixed, CdS-core/ZnS-shell NWs with a thicker shell would have larger band gap. The results agree well with that of red-shift or blue-shift of the spectrum in experimental observations. The partial density of states indicates that the contribution to valence band maximum mainly comes from the S-3p state, and the contribution to conduction band minimum mainly comes from Cd-5s state for CdS-core/ZnS-shell NWs. Thus the electrons would be effectively confined in CdS core, and the holes tend to distribute over both the core and shell. It can be deduced that CdS-core/ZnS-shell NWs with a thicker shell may have larger mobility.