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Xin Luo

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Published work

31 published item(s)

preprint2026arXiv

The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents

Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social dilemmas. Across 7 LLMs and 4 games over 500 rounds, expanding accessible history degrades cooperation in 18 of 28 model--game settings, a pattern we term the memory curse. We isolate the underlying mechanism through three analyses. First, lexical analysis of 378,000 reasoning traces associates this breakdown with eroding forward-looking intent rather than rising paranoia. We validate this using targeted fine-tuning as a cognitive probe: a LoRA adapter trained exclusively on forward-looking traces mitigates the decay and transfers zero-shot to distinct games. Second, memory sanitization holds prompt length fixed while replacing visible history with synthetic cooperative records, which restores cooperation substantially, proving the trigger is memory content, not length alone. Finally, ablating explicit Chain-of-Thought reasoning often reduces the collapse, showing that deliberation paradoxically amplifies the memory curse. Together, these results recast memory as an active determinant of multi-agent behavior: longer recall can either destabilize or support cooperation depending on the reasoning patterns it elicits.

preprint2023arXiv

A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images

Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust representations in an unsupervised manner from unlabelled biomedical microscopy images of low diversity in samples? and 2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework (TOWER) towards enhanced recognition of biomedical microscopy images, which works in three phases by synergizing contrastive learning and generative learning methods: 1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; 2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; 3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Experiments have been conducted on public datasets of biomedical microscopy images against the state-of-the-art counterparts (e.g., SimCLR and BYOL), and results demonstrate that: TOWER statistically excels in all self-supervised methods, achieving a Dice improvement of 1.38 percentage points over SimCLR. TOWER also has potential in multi-modality medical image analysis and enables label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.

preprint2022arXiv

A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data

High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a Sequential-Group-Differential- Evolution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. As demonstrated by the experiments on four HiDS matrices, a SGDE-PLFA model outperforms the state-of-the-art models.

preprint2022arXiv

A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data

High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method that extracts useful yet latent knowledge from HiDS matrices via low-rank approximation. Current LFA-based models mainly focus on a single-metric representation, where the representation strategy designed for the approximation Loss function, is fixed and exclusive. However, real-world HiDS matrices are commonly heterogeneous and inclusive and have diverse underlying patterns, such that a single-metric representation is most likely to yield inferior performance. Motivated by this, we in this paper propose a multi-metric latent factor (MMLF) model. Its main idea is two-fold: 1) two vector spaces and three Lp-norms are simultaneously employed to develop six variants of LFA model, each of which resides in a unique metric representation space, and 2) all the variants are ensembled with a tailored, self-adaptive weighting strategy. As such, our proposed MMLF enjoys the merits originated from a set of disparate metric spaces all at once, achieving the comprehensive and unbiased representation of HiDS matrices. Theoretical study guarantees that MMLF attains a performance gain. Extensive experiments on eight real-world HiDS datasets, spanning a wide range of industrial and science domains, verify that our MMLF significantly outperforms ten state-of-the-art, shallow and deep counterparts.

preprint2022arXiv

A New Calibration Method for Industrial Robot Based on Step-Size Levenberg-Marquardt Algorithm

Industrial robots play a vital role in automatic production, which have been widely utilized in industrial production activities, like handling and welding. However, due to an uncalibrated robot with machining tolerance and assembly tolerance, it suffers from low absolute positioning accuracy, which cannot satisfy the requirements of high-precision manufacture. To address this hot issue, we propose a novel calibration method based on an unscented Kalman filter and variable step-size Levenberg-Marquardt algorithm. This work has three ideas: a) proposing a novel variable step-size Levenberg-Marquardt algorithm to addresses the issue of local optimum in a Levenberg-Marquardt algorithm; b) employing an unscented Kalman filter to reduce the influence of the measurement noises; and c) developing a novel calibration method incorporating an unscented Kalman filter with a variable step-size Levenberg-Marquardt algorithm. Furthermore, we conduct enough experiments on an ABB IRB 120 industrial robot. From the experimental results, the proposed method achieves much higher calibration accuracy than some state-of-the-art calibration methods. Hence, this work is an important milestone in the field of robot calibration.

preprint2022arXiv

A New Robot Arm Calibration Method Based on Cubic Interpolated Beetle Antennae Search Approach

Industrial robot arms are extensively important for intelligent manufacturing. An industrial robot arm commonly enjoys its high repetitive positioning accuracy while suffering from its low absolute positioning accuracy, which greatly restricts its application in high-precision manufacture, like automobile manufacture. Aiming at addressing this hot issue, this work proposes a novel robot arm calibration method based on cubic interpolated beetle antennae search (CIBAS). This study has three ideas: a) developing a novel CIBAS algorithm, which can effectively addresses the issue of local optimum in a Beetle Antennae Search algorithm; b) utilizing a particle filter to reduce the influence of non-Gaussian noises; and c) proposing a new calibration method incorporating CIBAS algorithm and particle filter to searching the optimal kinematic parameters. Experimental results on an ABB IRB120 industrial robot arm demonstrate that the proposed method achieves much higher calibration accuracy than several state-of-the-art calibration methods.

preprint2022arXiv

A Novel Quadratic Interpolated Beetle Antennae Search for Manipulator Calibration

Over the past decades, industrial manipulators play a vital role in in various fields, like aircraft manufacturing and automobile manufacturing. However, an industrial manipulator without calibration suffers from its low absolute positioning accuracy, which extensively restricts its application in high-precision intelligent manufacture. Recent manipulator calibration methods are developed to address this issue, while they frequently encounter long-tail convergence and low calibration accuracy. To address this thorny issue, this work proposes a novel manipulator calibration method incorporating an extended Kalman filter with a Quadratic Interpolated Beetle Antennae Search algorithm. This paper has three-fold ideas: a) proposing a new Quadratic Interpolated Beetle Antennae Search algorithm to deal with the issue of local optimum and low convergence rate in a Beetle Antennae Search algorithm; b) adopting an extended Kalman filter algorithm to suppress non-Gaussian noises and c) developing a new manipulator calibration method incorporating an extended Kalman filter with a Quadratic Interpolated Beetle Antennae Search algorithm to calibrating a manipulator. Extensively experimental results on an ABB IRB120 industrial manipulator demonstrate that the proposed method achieves much higher calibration accuracy than several state-of-the-art calibration methods.

preprint2022arXiv

An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model

An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix. However, it introduces multiple hyper-parameters into the learning process, which should be chosen with care to enable its superior performance. Its hyper-parameter adaptation is desired for further enhancing its scalability. Targeting at this issue, this paper proposes an Adaptive Alternating-direction-method-based Nonnegative Latent Factor (A2NLF) model, whose hyper-parameter adaptation is implemented following the principle of particle swarm optimization. Empirical studies on nonnegative HDI matrices generated by industrial applications indicate that A2NLF outperforms several state-of-the-art models in terms of computational and storage efficiency, as well as maintains highly competitive estimation accuracy for an HDI matrix's missing data.

preprint2022arXiv

An Advancing Ensemble with Diversified Algorithms for Robot Arm Calibration

Recently, industrial robots plays a significant role in intelligent manufacturing. Hence, it is an urgent issue to ensure the robot with the high positioning precision. To address this hot issue, a novel calibration method based on an powerful ensemble with various algorithms is proposed. This paper has two ideas: a) developing eight calibration methods to identify the kinematic parameter errors; 2) establishing an effective ensemble to search calibrated kinematic parameters. Enough experimental results show that this ensemble can achieve: 1) higher calibration accuracy for the robot; 2) model diversity; 3) strong generalization ability.

preprint2022arXiv

Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices

A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform highly accurate representation learning on it is of great significance owing to the great desire of extracting latent knowledge and patterns from it. Latent factor analysis (LFA), which represents an HiDS matrix by learning the low-rank embeddings based on its observed entries only, is one of the most effective and efficient approaches to this issue. However, most existing LFA-based models perform such embeddings on a HiDS matrix directly without exploiting its hidden graph structures, thereby resulting in accuracy loss. To address this issue, this paper proposes a graph-incorporated latent factor analysis (GLFA) model. It adopts two-fold ideas: 1) a graph is constructed for identifying the hidden high-order interaction (HOI) among nodes described by an HiDS matrix, and 2) a recurrent LFA structure is carefully designed with the incorporation of HOI, thereby improving the representa-tion learning ability of a resultant model. Experimental results on three real-world datasets demonstrate that GLFA outperforms six state-of-the-art models in predicting the missing data of an HiDS matrix, which evidently supports its strong representation learning ability to HiDS data.

preprint2022arXiv

High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection

Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle. To date, a Symmetric and Non-negative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing to its high interpretability and scalability. However, most existing SNMF-based community detection methods neglect the high-order connection patterns in a network. Motivated by this discovery, in this paper, we propose a High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regularized NMF (HSGN) model that adopts the following three-fold ideas: a) adopting a weighted pointwise mutual information (PMI)-based approach to measure the HOP indices among nodes in a network; b) leveraging an iterative reconstruction scheme to encode the captured HOP into the network; and c) introducing a symmetry and graph-regularized NMF algorithm to detect communities accurately. Extensive empirical studies on eight real-world networks demonstrate that an HSGN-based community detector significantly outperforms both benchmark and state-of-the-art community detectors in providing highly-accurate community detection results.

preprint2022arXiv

Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data

With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already available in supplementary material and will be made publicly available.

preprint2022arXiv

Second-order Symmetric Non-negative Latent Factor Analysis

Precise representation of large-scale undirected network is the basis for understanding relations within a massive entity set. The undirected network representation task can be efficiently addressed by a symmetry non-negative latent factor (SNLF) model, whose objective is clearly non-convex. However, existing SNLF models commonly adopt a first-order optimizer that cannot well handle the non-convex objective, thereby resulting in inaccurate representation results. On the other hand, higher-order learning algorithms are expected to make a breakthrough, but their computation efficiency are greatly limited due to the direct manipulation of the Hessian matrix, which can be huge in undirected network representation tasks. Aiming at addressing this issue, this study proposes to incorporate an efficient second-order method into SNLF, thereby establishing a second-order symmetric non-negative latent factor analysis model for undirected network with two-fold ideas: a) incorporating a mapping strategy into SNLF model to form an unconstrained model, and b) training the unconstrained model with a specially designed second order method to acquire a proper second-order step efficiently. Empirical studies indicate that proposed model outperforms state-of-the-art models in representation accuracy with affordable computational burden.

preprint2022arXiv

Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval

The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a challenging task because of the large domain gap between sketches and natural images as well as the semantic inconsistency between seen and unseen categories. Previous literature bridges seen and unseen categories by semantic embedding, which requires prior knowledge of the exact class names and additional extraction efforts. And most works reduce domain gap by mapping sketches and natural images into a common high-level space using constructed sketch-image pairs, which ignore the unpaired information between images and sketches. To address these issues, in this paper, we propose a novel Three-Stream Joint Training Network (3JOIN) for the ZS-SBIR task. To narrow the domain differences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches. For exploiting a sufficient combination of sketches, natural images, and edge maps, a novel three-stream joint training network is proposed. In addition, we use a teacher network to extract the implicit semantics of the samples without the aid of other semantics and transfer the learned knowledge to unseen classes. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed method.

preprint2022arXiv

ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator

Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC).These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks.However, there are some limitations when applying ViT directly to FGVC.First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy redundant calculation and unsatisfying performance when handling fine-grained images with complex background and small objects.Second, a standard ViT only utilizes the class token in the final layer for classification, which is not enough to extract comprehensive fine-grained information. To address these issues, we propose a novel ViT based fine-grained object discriminator for FGVC tasks, ViT-FOD for short. Specifically, besides a ViT backbone, it further introduces three novel components, i.e, Attention Patch Combination (APC), Critical Regions Filter (CRF), and Complementary Tokens Integration (CTI). Thereinto, APC pieces informative patches from two images to generate a new image so that the redundant calculation can be reduced. CRF emphasizes tokens corresponding to discriminative regions to generate a new class token for subtle feature learning. To extract comprehensive information, CTI integrates complementary information captured by class tokens in different ViT layers. We conduct comprehensive experiments on widely used datasets and the results demonstrate that ViT-FOD is able to achieve state-of-the-art performance.

preprint2021arXiv

Far-Field Super-Resolution Imaging By Nonlinear Excited Evanescent Waves

Abbe's resolution limit, one of the best-known physical limitations, poses a great challenge for any wave systems in imaging, wave transport, and dynamics. Originally formulated in linear optics, this Abbe's limit can be broken using nonlinear optical interactions. Here we extend the Abbe theory into a nonlinear regime and experimentally demonstrate a far-field, label-free, and scan-free super-resolution imaging technique based on nonlinear four-wave mixing to retrieve near-field scattered evanescent waves, achieving sub-wavelength resolution of $λ/15.6$. This method paves the way for application in biomedical imaging, semiconductor metrology, and photolithography.

preprint2020arXiv

Dual Graph Representation Learning

Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs. Although inductive approaches can generalize to unseen nodes, they neglect different contexts of nodes and cannot learn node embeddings dually. In this paper, we present a context-aware unsupervised dual encoding framework, \textbf{CADE}, to generate representations of nodes by combining real-time neighborhoods with neighbor-attentioned representation, and preserving extra memory of known nodes. We exhibit that our approach is effective by comparing to state-of-the-art methods.

preprint2020arXiv

Khintchine inequality on normed spaces and the application to Banach-Mazur distance

We establish variant Khintchine inequalities on normed spaces of Hanner type and cotype, in which the Rademacher distribution corresponding to classical Khintchine inequality is replaced by general symmetric distributions. The proof involves the $p$-barycenter and Birkhoff's ergodic theorem. More importantly, by employing these Khintchine inequalities, we get some lower bounds for Banach-Mazur distance between $l^p$-ball and a general centrally symmetric convex body.

preprint2020arXiv

Origin and Electronic Behavior of Improper Ferroelectricity in AB2 (A=Cr, Mo, W; B=S, Se, Te) Transition Metal Dichalcogenides

Persistent electrical polarized states are fundamentally important to the electric industry as they can be used in the non-volatile memory, the artificial neuromorphic network, and negative capacitors, making ultralow energy consumption electronic devises possible. With the recent development in low dimensional ferroelectric materials, emerging 2D out-of-plane ferroelectric materials like MoTe2 have great potential for future development. Despite previous phenomenological studies, the underlying microscopic origin of ferroelectricity is still missing. Here, using density functional theory and Wannier function methods, we reveal that the origin of ferroelectricity of these transition metal dichalcogenides comes from the Jahn taller effect and followed by a covalent bonding between transition metal atoms. Moreover, the atypical electronic behavior of these monolayer AB2 (A=Cr, Mo, W; B=S, Se, Te) TMDs compare to traditional improper ferroelectrics hints a strong electronic origin of the polarization, suitable for future industrial applications.

preprint2020arXiv

Spectrum of signless 1-Laplacian on simplicial complexes

We introduce the signless 1-Laplacians and the dual Cheeger constants on simplicial complexes. The connection of its spectrum to the combinatorial properties like independence number, chromatic number and dual Cheeger constant is investigated. Our estimates can be comparable to Hoffman's bounds in virtue of Laplacian on simplicial complexes. An interesting inequality relating multiplicity of the largest eigenvalue, independence number and chromatic number are provided, which could be regarded as a variant version of Lovasz sandwich theorem. Also, the behavior of the operator under the topological operations of wedge and duplication of motifs is studied. The Courant nodal domain theorem in spectral theory is extended to the case of signless 1-Laplacian on complexes.

preprint2020arXiv

Theoretical investigation of two-dimensional phosphorus carbides as promising anode materials for lithium-ion batteries

Employing two-dimensional (2D) materials as anodes for lithium-ion batteries (LIBs) is believed to be an effective approach to meet the growing demands of high-capacity next-generation LIBs. In this work, the first-principles density functional theory (DFT) calculations are employed to evaluate the potential application of two-dimensional phosphorus carbide (2D PCx, x=2, 5, and 6) monolayers as anode materials for lithium-ion batteries. The 2D PCx systems are predicted to show outstanding structural stability and electronic properties. From the nudge elastic band calculations, the Li atoms show extreme high diffusivities on the PCx monolayer with low energy barriers of 0.18 eV for PC2, 0.47 eV for PC5, and 0.44 eV for PC6. We further demonstrate that the theoretical specific capacity of monolayer PC5 and PC6 can reach up to 1251.7 and 1235.9 mAh g-1, respectively, several times that of graphite anode used in commercial LIBs. These results suggest that both PC5 and PC6 monolayer are promising anode materials for LIBs. Our work opens a new avenue to explore novel 2D materials in energy applications, where phosphorus carbides could be used as high-performance anode in LIBs.

preprint2020arXiv

Transition Metal-Tetracyanoquinodimethane Monolayers as Single-Atom Catalysts for Electrocatalytic Nitrogen Reduction Reaction

Converting earth-abundant dinitrogen into value-added chemical ammonia is a significant yet challenging topic. Electrocatalytic nitrogen reduction reaction (NRR), compared with conventional Haber-Bosch process, is an energy-saving and environmentally friendly approach. The major task of electrocatalytic NRR is to find electrocatalysts which can activate dinitrogen effectively and exhibit high selectivity and stability. Single atom catalysts can act as a good solution. In this work, by means of first-principles density functional theory, molecular dynamics calculations, and a two-step screening process, we confirm that single Sc and Ti atom supported on tetracyanoquinodimethane monolayers (Sc,Ti-TCNQ) are excellent candidates for NRR electrocatalysts. N2 adsorption and activation are effective due to the acceptance-donation mechanism and outstanding electronic structure of TM-TCNQ, and Gibbs free energy diagram shows that Sc-TCNQ and Ti-TCNQ exhibit low NRR overpotential of 0.33 and 0.22 V through enzymatic-consecutive mixed pathway, respectively. In addition, selectivity over HER and stability of Sc/Ti-TCNQ monolayers are also validated. This work opens a new avenue for designing novel single atom catalysts for NRR as well as other catalytic applications.

preprint2015arXiv

Large Frequency Change with Thickness in Interlayer Breathing Mode - Significant Interlayer Interactions in Few Layer Black Phosphorus

Bulk black phosphorus (BP) consists of puckered layers of phosphorus atoms. Few-layer BP, obtained from bulk BP by exfoliation, is an emerging candidate as a channel material in post-silicon electronics. A deep understanding of its physical properties and its full range of applications are still being uncovered. In this paper, we present a theoretical and experimental investigation of phonon properties in few-layer BP, focusing on the low-frequency regime corresponding to interlayer vibrational modes. We show that the interlayer breathing mode A3g shows a large redshift with increasing thickness; the experimental and theoretical results agreeing well. This thickness dependence is two times larger than that in the chalcogenide materials such as few-layer MoS2 and WSe2, because of the significantly larger interlayer force constant and smaller atomic mass in BP. The derived interlayer out-of-plane force constant is about 50% larger than that in graphene and MoS2. We show that this large interlayer force constant arises from the sizable covalent interaction between phosphorus atoms in adjacent layers, and that interlayer interactions are not merely of the weak van der Waals type. These significant interlayer interactions are consistent with the known surface reactivity of BP, and have been shown to be important for electric-field induced formation of Dirac cones in thin film BP.

preprint2015arXiv

On the Hyperbolizing metric spaces

In this paper, we prove that the metric space $(Z\setminus M,u_Z)$ defined by Z.Ibragimov is asymptotically $PT_{-1}$ if the metric space $(Z,d)$ is $PT_{0}$, where $M$ is a nonempty closed proper subset of $Z$. Secondly, based on the metric $u_Z$, we define a new kind of metric $k_{z}$ on the set $Z\setminus M$ and show that the new metric space $(Z\setminus M,k_{Z})$ is also asymptotically $PT_{-1}$ without the assumption of $PT_{0}$ on the metric space $(Z,d)$.

preprint2015arXiv

Stacking sequence determines Raman intensities of observed interlayer shear modes in 2D layered materials - A general bond polarizability model

2D layered materials have recently attracted tremendous interest due to their fascinating properties and potential applications. The interlayer interactions are much weaker than the intralayer bonds, allowing the as-synthesized materials to exhibit different stacking sequences (e.g. ABAB, ABCABC), leading to different physical properties. Here, we show that regardless of the space group of the 2D material, the Raman frequencies of the interlayer shear modes observed under the typical configuration blue shift for AB stacked materials, and red shift for ABC stacked materials, as the number of layers increases. Our predictions are made using an intuitive bond polarizability model which shows that stacking sequence plays a key role in determining which interlayer shear modes lead to the largest change in polarizability (Raman intensity); the modes with the largest Raman intensity determining the frequency trends. We present direct evidence for these conclusions by studying the Raman modes in few layer graphene, MoS2, MoSe2, WSe2 and Bi2Se3, using both first principles calculations and Raman spectroscopy. This study sheds light on the influence of stacking sequence on the Raman intensities of intrinsic interlayer modes in 2D layered materials in general, and leads to a practical way of identifying the stacking sequence in these materials.

preprint2015arXiv

Tuning the Threshold Voltage of MoS2 Field-Effect Transistors via Surface Treatment

Controlling the threshold voltage (Vth) of a field-effect transistor is important for realizing robust logic circuits. Here, we report a facile approach to achieve bidirectional Vth tuning of molybdenum disulfide (MoS2) field-effect transistors. By increasing and decreasing the amount of sulfur vacancies in the MoS2 surface, the Vth of MoS2 transistors can be left- and right-shifted, respectively. Transistors fabricated on perfect MoS2 flakes are found to exhibit two-fold enhancement in mobility and a very positive Vth. More importantly, our elegant hydrogen treatment is able to tune the large Vth to a small value without any performance degradation simply by reducing the atomic ratio of S:Mo slightly; in other words, creating a certain amount of sulfur vacancies in the MoS2 surface, which generate defect states in the band gap of MoS2 that mediate conduction of a MoS2 transistor in the subthreshold regime. First-principles calculations further indicate that the edge and width of defect band can be tuned according to the vacancy density. This work not only demonstrates for the first time the ease in tuning the Vth of MoS2 transistors, but also offers a process technology solution that is critical for further development of MoS2 as a mainstream electronic material.

preprint2014arXiv

Effects of Lower Symmetry and Dimensionality on Raman Spectra in 2D WSe2

We report the observation and interpretation of new Raman peaks in few-layer tungsten diselenide (WSe2), induced by the reduction of symmetry going from 3D to 2D. In general, Raman frequencies in 2D materials follow quite closely the frequencies of corresponding eigenmodes in the bulk. However, while the modes that are Raman active in the bulk are also Raman active in the thin films, the reverse is not always true due to the reduced symmetry in thin films. Here, we predict from group theory and density functional calculations that two intra-layer vibrational modes which are Raman inactive in bulk WSe2 in our experimental configuration become Raman active in thin film WSe2, due to reduced symmetry in thin films. This phenomenon explains the Raman peaks we observe experimentally at ~310 cm-1 and 176 cm-1 in thin film WSe2. Interestingly, the bulk mode at ~310 cm-1 that is Raman inactive can in fact be detected in Raman measurements under specific wavelengths of irradiation, suggesting that in this case, crystal symmetry selection rules may be broken due to resonant scattering. Both theory and experiment indicate that the and modes blue-shift with decreasing thickness, which we attribute to surface effects. Our results shed light on a general understanding of the Raman/IR activities of the phonon modes in layered transition metal dichalcogenide materials and their evolution behavior from 3D to 2D.

preprint2014arXiv

Low Resistance Metal Contacts to MoS2 Devices with Nickel-Etched-Graphene Electrodes

We report an approach to achieve low-resistance contacts to MoS2 transistors with the intrinsic performance of the MoS2 channel preserved. Through a dry transfer technique and a metal-catalyzed graphene treatment process, nickel-etched-graphene electrodes were fabricated on MoS2 that yield contact resistance as low as 200 ohm-um. The substantial contact enhancement (~2 orders of magnitude) as compared to pure nickel electrodes, is attributed to the much smaller work function of nickel-graphene electrodes, together with the fact that presence of zigzag edges in the treated graphene surface enhances tunneling between nickel and graphene. To this end, the successful fabrication of a clean graphene-MoS2 interface and a low resistance nickel-graphene interface is critical for the experimentally measured low contact resistance. The potential of using graphene as an electrode interlayer demonstrated in this work paves the way towards achieving high performance next-generation transistors.

preprint2013arXiv

Anomalous Frequency Trends in MoS2 Thin Films Attributed to Surface Effects

The layered dichalcogenide MoS2 has many unique physical properties in low dimensions. Recent experimental Raman spectroscopies report an anomalous blue shift of the in-plane E2g1 mode with decreasing thickness, a trend that is not understood. Here, we combine experimental Raman scattering and theoretical studies to clarify and explain this trend. Special attention is given to understanding the surface effect on Raman frequencies by using a force constants model based on first-principles calculations. Surface effects refer to the larger Mo-S force constants at the surface of thin film MoS2, which results from a loss of neighbours in adjacent MoS2 layers. Without surface effects, the frequencies of both out-of-plane A1g and in-plane E2g1 modes decrease with decreasing thickness. However, the E2g1 mode blue shifts while the A1g mode red shifts once the surface effect is included, in agreement with the experiment. Our results show that competition between the thickness effect and the surface effect determines the mechanical properties of two-dimensional MoS2, which we believe applies to other layered materials.

preprint2013arXiv

First Principles Investigations of the Atomic, Electronic, and Thermoelectric Properties of Equilibrium and Strained Bi2Se3 & Bi2Te3, with van der Waals Interactions

Bi2Se3 and Bi2Te3 are layered compounds of technological importance, being excellent thermoelectric materials as well as topological insulators. We report density functional theory calculations of the atomic, electronic and thermoelectric properties of strained bulk and thin film Bi2Se3 and Bi2Te3, focusing on an appropriate description of van der Waals interactions. The calculations show that the van der Waals Density functional with Cooper's exchange can reproduce closely the experimental interlayer distances in unstrained Bi2Se3 and Bi2Te3. Interestingly, we predict atomic structures that are in much better agreement with the experimentally determined structure from Nakajima than that obtained from Wyckoff, especially for Bi2Se3 where the difference in atomic structures qualitatively changes the electronic band structure. The band structure obtained using the Nakajima structure, and the theoretically optimized structure, are in much better agreement with previous reports of photoemission measurements, than that obtained using the Wyckoff structure. Fully optimizing atomic structures of bulk and thin film Bi2Se3 and Bi2Te3 under different in-plane and uniaxial strains, we predict that the electronic band gap of both the bulk materials and thin films decreases with tensile in-plane strain and increases with compressive in-plane strain. We also predict, using the semiclassical Boltzmann approach, that the magnitude of the n-type Seebeck coefficient of Bi2Te3 can be increased by the compressive in-plane strain, while that of Bi2Se3 can be increased with tensile in-plane strain. Further, the in-plane power factor of n-doped Bi2Se3 can be increased with compressive uniaxial strain, while that of n-doped Bi2Te3 can be increased by compressive in-plane strain. ...

preprint2013arXiv

Interlayer breathing and shear modes in few-trilayer MoS2 and WSe2

Two-dimensional (2D) layered transition metal dichalcogenides (TMDs) have recently attracted tremendous interest as potential valleytronic and nano-electronic materials, in addition to being well-known as excellent lubricants in the bulk. The interlayer van der Waals (vdW) coupling and low frequency phonon modes, and how they evolve with the number of layers, are important for both the mechanical and electrical properties of 2D TMDs. Here we uncover the ultra-low frequency interlayer breathing and shear modes in few-layer MoS2 and WSe2, prototypical layered TMDs, using both Raman spectroscopy and first principles calculations. Remarkably, the frequencies of these modes can be perfectly described using a simple linear chain model with only nearest-neighbour interactions. We show that the derived in-plane (shear) and out-of-plane (breathing) force constants from experiment remain the same from two-layer 2D crystals to the bulk materials, suggesting that the nanoscale interlayer frictional characteristics of these excellent lubricants should be independent of the number of layers.