Lower Dimension Research Articles

Assessment of Roughness Characterization Methods for Data-Driven Predictions

A comparative analysis is undertaken to explore the impact of various roughness characterization methods as input variables on the performance of data-driven predictive models for estimating the roughness equivalent sand-grain size ks\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k_s$$\\end{document}. The first type of model, denoted as ENNPS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {ENN}_\ ext {PS}$$\\end{document}, incorporates the roughness height probability density function (p.d.f.) and power spectrum (PS), while the second type of model, ENNPA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {ENN}_\ ext {PA}$$\\end{document}, utilizes a finite set of 17 roughness statistical parameters as input variables. Furthermore, a simplified parameter-based model, denoted as ENNPAM\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {ENN}_\ ext {PAM}$$\\end{document}, is considered, which features only 6 input roughness parameters. The models are trained based on identical databases and evaluated using roughness samples similar to the training databases as well as an external testing database based on literature. While the predictions based on p.d.f. and PS achieves a stable error level of around 10% among all considered testing samples, a notable deterioration in performance is observed for the parameter-based models for the external testing database, indicating a lower extrapolating capability to diverse roughness types. Finally, the sensitivity analysis on different types of roughness confirms an effective identification of distinct roughness effects by ENNPAM\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {ENN}_\ ext {PAM}$$\\end{document}, which is not observed for ENNPA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {ENN}_\ ext {PA}$$\\end{document}. We hypothesize that the successful training of ENNPAM\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {ENN}_\ ext {PAM}$$\\end{document} is attributed to the enhanced training efficiency linked to the lower input dimensionality.

Assessment of Upper and Lower Airway Dimensions in Skeletal Class I and Class II Subjects Having Different Growth Patterns - A Cross-Sectional Cephalometric Study.

To compare the airway dimensions among subjects having skeletal class I and class II patterns considering their hypodivergent, normodivergent, and hyperdivergent growth patterns and to determine any variations between them. The study consisted of 156 pre-treatment lateral cephalograms of subjects aged above 18 years. The sagittal skeletal pattern was used to separate the sample into two groups. Each group was further subdivided into hypodivergent, normodivergent, and hyperdivergent growth patterns. The upper and lower airway dimensions were measured in all the subgroups. The intragroup comparison of class I and class II was performed with 1-way ANOVA and post-hoc test. Using the independent t-test, the upper and lower airways were compared between groups. Comparison of subjects according to the growth pattern showed that vertical growth patterns have statically significantly reduced upper and lower airway dimensions as compared to the average and horizontal growth patterns in both skeletal class I and class II subjects. However, the subjects with skeletal class II hyperdivergent growth patterns have significantly reduced lower airway dimensions than those with skeletal class I having hyperdivergent growth patterns. Individuals with skeletal class I and class II patterns characterized by hyperdivergent growth exhibit reduced upper and lower pharyngeal airways. The sagittal skeletal type also plays a role in influencing the dimensions of the upper and lower airways.

Structure Low Dimensionality and Lone-Pair Stereochemical Activity: the Key to Low Thermal Conductivity in the Pb-Sn-S System.

Recently, metal sulfides have begun to receive attention as potential cost-effective materials for thermoelectric applications beyond optoelectronic and photovoltaic devices. Herein, based on a comparative analysis of the structural and transport properties of 2D PbSnS2 and 1D PbSnS3, we demonstrate that the intrinsic effects that govern the low lattice thermal conductivity (κL) of these sulfides originate from the combination of the low dimensionality of their crystal structures with the stereochemical activity of the lone-pair electrons of cations. The presence of weak bonds in these materials, responsible for phonon scattering, results in inherently low κL of 1.0 W/m K in 1D PbSnS3 and 0.6 W/m K in 2D PbSnS2 at room temperature. However, the nature of the thermal transport is quite distinct. 1D PbSnS3 exhibits a higher thermal conductivity with a crystalline-like peak at low temperatures, while 2D PbSnS2 demonstrates glassy thermal conductivity in the entire temperature range investigated. First-principles density functional theory calculations reveal that the presence of antibonding states below the Fermi level, especially in PbSnS2, contributes to the very low κL. In addition, the calculated phonon dispersions exhibit very soft acoustic phonon branches that give rise to soft lattices and very low speeds of sounds.

An initialization approach for metaheuristic algorithms by using Gibbs sampling

Recently, several new initialization techniques have been proposed. Despite their good results in very low dimensions, their performance deteriorated significantly with an increase in the number of dimensions. This paper introduces a new method for initializing metaheuristic algorithms, based on the Gibbs sampling approach. The proposed method samples according to the Gibbs method, a multidimensional Gaussian that completely covers the search space defined by the objective function. In this process, each decision variable is sequentially sampled to ensure that the value of each is dependent only on the value of the variable sampled before it. This process leads to the generation of initial positions with significantly low mutual correlation, which is an advantageous feature that prevents the aggregation of initial solutions in specific areas of the search space. This issue is particularly prevalent in optimization problems with a greater number of dimensions. To test the effectiveness of this method, it was applied in conjunction with a Differential Evolution algorithm. The complete approach has been evaluated using a selection of pertinent and challenging functions in the field. The outcome of these experiments showed that the algorithm can establish a superior set of initial solutions, which allows consistent determination of the global solution, even as the complexity of the problem increases with more dimensions.

THE EFFECT OF DIGITAL LITERACY LEVELS OF HEALTH MANAGERS AND EMPLOYEES ON JOB SATISFACTION: THE CASE OF SAKARYA

Increasing internet usage enables individuals to easily access the information they are searching for. This situation brings up the concept of digital literacy. Healthcare institutions are also increasingly keeping up with this internet age. In this study, it was tried to reveal the difference between the socio-demographic characteristics of healthcare managers and employees working in healthcare institutions and the lower dimensions of digital literacy and job satisfaction. This study also aims to investigate the impact of digital literacy lower dimensions on job satisfaction's lower dimensions and make suggestions. The research was carried out with the participation of health managers and employees consisting of 396 people working in Sakarya University Training and Research Hospital. The data were analyzed using the SPSS 27.0 package program. "Independent Sample T-Test" was used to reveal the differences between two groups, and "One Way ANOVA Test" was used to reveal the differences between three or more groups. Tukey Test, one of the Post-Hoc tests, was used to determine the source of the difference. Correlation Analysis was used to determine the relationship in the research, and Multiple Regression Analysis was used to reveal the effect. According to the research results, a low-level positive relationship was found between digital literacy lower dimensions and internal and external satisfaction (p0.001). In the research, it was determined that the lower dimensions of digital literacy do not differ according to gender, marital status and age, but vary according to the position in the institution. While there is a significant difference between internal satisfaction, which is a lower dimension of job satisfaction, and gender, age and position in the institution, there is a significant difference between external satisfaction and only the position in the institution. It is thought that the offered service will be provided more efficiently and effectively by eliminating the deficiencies determined as a result of the analysis in the institution where the study is conducted.

Functionalization and Structural Evolution of Conducting Quasi-One-Dimensional Chevrel-Type Telluride Nanocrystals.

Interfacing organic molecular groups with well-defined inorganic lattices, especially in low dimensions, enables synthetic routes for the rational manipulation of both their local or extended lattice structures and physical properties. While appreciably studied in two-dimensional systems, the influence of surface organic substituents on many known and emergent one-dimensional (1D) and quasi-1D (q-1D) crystals has remained underexplored. Herein, we demonstrate the surface functionalization of bulk and nanoscale Chevrel-like q-1D ionic crystals using In2Mo6Te6, a predicted q-1D Dirac semimetal, as the model phase. Using a series of alkyl ammonium (-NR4+; R = H, methyl, ethyl, butyl, and octyl) substituents with varying chain lengths, we demonstrate the systematic expansion of the intrachain c-axis direction and the contraction of the interchain a/b-axis direction with longer chain substituents. Additionally, we demonstrate the systematic expansion of the intrachain c-axis direction and the contraction of the interchain a/b-axis direction as the alkyl chain substituents become longer using a combination of powder X-ray diffraction and Raman experiments. Beyond the structural modulation that the substituted groups can impose on the lattice, we also found that the substitution of ammonium-based groups on the surface of the nanocrystals resulted in selective suspension in aqueous (NH4+-functionalized) or organic solvents (NOc4+-functionalized), imparted fluorescent character (Rhodamine B-functionalized), and modulated the electrical conductivity of the nanocrystal ensemble. Altogether, our results underscore the potential of organic-inorganic interfacing strategies to tune the structural and physical properties of rediscovered Chevrel-type q-1D ionic solids and open opportunities for the development of surface-addressable building blocks for hybrid electronic and optoelectronic devices at the nanoscale.

Frostman lemma revisited

We study sharpness of various generalizations of Frostman's lemma. These generalizations provide better estimates for the lower Hausdorff dimension of measures. As a corollary, we prove that if a generalized anisotropic gradient \((\partial_1^{m_1} f, \partial_2^{m_2} f,\ldots, \partial_d^{m_d} f)\) of a function \(f\) in \(d\) variables is a measure of bounded variation, then this measure is absolutely continuous with respect to the Hausdorff \(d-1\) dimensional measure.

A Cross-Language Comparative Study of English and German Vowel Space Area in Swiss German L1 Speakers

It is conventional to examine vowel systems by their acoustic correlates, specifically formants. In the International Phonetic Alphabet (IPA) vowel chart, vowels are placed according to their relative positions determined by the first two formant values. This two-dimensional plane makes possible the calculation of the articulatory working space of vowels. Variations in the size and shape of the vowel space exist across gender, dialects, and languages. The present study focuses on comparing the vowel space area (VSA) in English and German produced by native Swiss German speakers, attempting to shed light on whether VSAs differ significantly with different calculation methods cross-linguistically. Given the fact that absolute formant values vary tremendously across gender, Hertz values are converted to z-scores, allowing for more direct and meaningful comparisons. While vowel normalization mitigates gender-related differences within specific languages to a large extent, there remains a significant cross-linguistic gender difference. The results reveal that for 3- and 4-vowel space there is no significant effect in language but an effect is observed in gender and vowel combination on VSA, specifically in terms of individual area and overlapping ratio. This suggests that Swiss German speakers tend to utilize the same amount of vowel space in both languages, with the only difference being the expansion of English VSA in higher F2 dimension and lower F1 dimension, as observed across all calculation methods.

Adaptive Deep Fourier Residual method via overlapping domain decomposition

The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural network (VPINN). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on minimizing the dual norm of the weak residual of a PDE, which is equivalent to minimizing the energy norm of the error. To compute the dual norm of the weak residual, the DFR method employs an orthonormal spectral basis of the test space, known for rectangles or cuboids for multiple function spaces.In this work, we extend the DFR method with ideas of traditional domain decomposition (DD). This enables two improvements: (a) to solve problems in more general polygonal domains and (b) to develop an adaptive refinement technique in the test space using a Döfler marking algorithm. In the former case, we retain the desirable equivalence between the employed loss function and the H1 error under non-restrictive assumptions, numerically demonstrating adherence to explicit bounds in the case of the L-shaped domain problem. In the latter, we show how refinement strategies lead to potentially significant improvements against a reference, classical DFR implementation with a test function space of significantly lower dimensionality, allowing us to better approximate singular solutions at a more reasonable computational cost.

On the Integrability of Persistent Quadratic Three-Dimensional Systems

We consider a nine-parameter familiy of 3D quadratic systems, x˙=x+P2(x,y,z), y˙=−y+Q2(x,y,z), z˙=−z+R2(x,y,z), where P2,Q2,R2 are quadratic polynomials, in terms of integrability. We find necessary and sufficient conditions for the existence of two independent first integrals of corresponding semi-persistent, weakly persistent, and persistent systems. Unlike some of the earlier works, which primarily focus on planar systems, our research covers three-dimensional spaces, offering new insights into the complex dynamics that are not typically apparent in lower dimensions.

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs.

Adaptive penalty-based neurodynamic approach for nonsmooth interval-valued optimization problem

The complex and diverse practical background drives this paper to explore a new neurodynamic approach (NA) to solve nonsmooth interval-valued optimization problems (IVOPs) constrained by interval partial order and more general sets. On the one hand, to deal with the uncertainty of interval-valued information, the LU-optimality condition of IVOPs is established through a deterministic form. On the other hand, according to the penalty method and adaptive controller, the interval partial order constraint and set constraint are punished by one adaptive parameter, which is a key enabler for the feasibility of states while having a lower solution space dimension and avoiding estimating exact penalty parameters. Through nonsmooth analysis and Lyapunov theory, the proposed adaptive penalty-based neurodynamic approach (APNA) is proven to converge to an LU-solution of the considered IVOPs. Finally, the feasibility of the proposed APNA is illustrated by numerical simulations and an investment decision-making problem.

First-Principles and Experimental Investigation of ABO4 Zircons as Calcium Intercalation Cathodes.

Identifying next-generation batteries with multivalent ions, such as Ca2+ is an active area of research to meet the increasing demand for large-scale, renewable energy storage solutions. Despite the promise of higher energy densities with multivalent batteries, one of their main challenges is addressing the sluggish kinetics in cathodes that arise from stronger electrostatic interactions between the multivalent ion and host lattice. In this paper, zircons are theoretically and experimentally evaluated as Ca cathodes. A migration barrier as low as 113 meV is computationally found in YVO4, which is the lowest Ca2+ barrier reported to date. Low barriers are confirmed across 18 zircon compositions, which are related to the low coordination change and reduced interstitial site preference of Ca2+ along the diffusion pathway. Among the four materials (BiVO4, YVO4, EuCrO4, and YCrO4) that were synthesized, characterized, and electrochemically cycled, the highest initial capacity of 81 mA h/g and the most reversible capacity of 65 mA h/g were achieved in YVO4 and BiVO4, respectively. Despite the facile migration of multivalent ions in zircons, density functional theory predictions of the unstable, discharged structures at higher Ca2+ concentrations (Cax>0.25ABO4), the low dimensionality of the migration pathway, and the defect analysis of the B site atom can rationalize the limited intercalation observed upon electrochemical cycling.

Energy Efficient Predictive Maintenance in Resource Constraint Industrial IoT devices using Smoothed Mish Activation Function

Traditionally, Industrial IoT devices collect sensor data to a cloud platform where ML/DL processing is to be done. Edge computing has the advantage of reducing latency, improved battery performance, safe transmission and reducing vulnerability. These benefits are particularly significant considering the limited resources on IoT devices, such as only a few kilobytes of RAM, and the critical importance of energy savings in industrial applications. Arduino Uno uses 2kB of RAM and 32 kB of read only flash memory. Optimal performance is required for ML inference on EDGE devices to get good accuracies and low latency. This research paper is based on memory optimization in resource constrained devices, focusing on a novel approach that combines the Bonsai tree architecture with a smoothed Mish activation function. While the Bonsai tree architecture has been explored in prior works, this research contributes to the refinement of the activation function, a crucial component in achieving superior test accuracy within limited memory constraints. Although many algorithms have been developed with good accuracy but can’t be used in optimal power consumption with low latency and resource constraint like fewer RAM and ROM. A smooth Mish activation function in Bonsai tree algorithm is proposed to enhance accuracy with lowest ML model size, where Bonsai tree is a single,shallow,sparse tree where data is projected in low dimension space. Lowest ML model size consumes least power. We observed the smoothed Mish activation function in the Bonsai tree algorithm outperform with low latency and power consumption. This paper aims to elucidate the methodology, experimental results, and implications of the modified activation function, shedding light on its potential to revolutionize memory-efficient edge computing in the Industrial IoT network.

Universal p -wave tetramers in low-dimensional fermionic systems with three-body interaction

Inspired by the narrow Feshbach resonance in systems with the two-body interaction, we propose a two-channel model of three-component fermions with a three-body interaction that takes into account finite-range effects in low dimensions. Within this model, the p-wave Efimov-like effect in the four-body sector is predicted in fractional dimensions between d=1 and d=2. The impact of the finite-range interaction on the formation of the four-body bound states in d=1 is also discussed in detail. Published by the American Physical Society 2024

Electrically tunable correlated domain wall network in twisted bilayer graphene

We investigate the domain wall network in twisted bilayer graphene (TBG) under the influence of interlayer bias and screening effect from the layered structure. Starting from the continuum model, we analyze the low-energy domain wall modes within the moiré bilayer structure and obtain an analytic form representing charge density distributions of the two-dimensional structure. By computing the screened electron–electron interaction strengths both within and between the domain walls, we develop a bosonized model that describes the correlated domain wall network. We demonstrate that these interaction strengths can be modified through an applied interlayer bias, screening length and dielectric materials, and show how the model can be employed to investigate various properties of the domain wall network and its stability. We compute correlation functions both without and with phonons. Including electron–phonon coupling in the network, we establish phase diagrams from these correlation functions. These diagrams illustrate electrical tunability of the network between various phases, such as density wave states and superconductivity. Our findings reveal the domain wall network as a promising platform for the experimental manipulation of electron–electron interactions in low dimensions and the study of strongly correlated matter. We point out that our investigation not only enhances the understanding of domain wall modes in TBG but also has broader implications for the development of moiré devices.

One-dimensional D-π-A conjugated polymers for photoenhanced gold recovery from e-waste

In principle, photocatalytic Au(III) reduction from e-waste using sunlight could provide a cleaner and more efficient alternative strategy to existing processes. Two-dimensional (2D) conjugated polymers are often employed for photocatalytic systems due to their structural diversity and stability. However, the efficiency of active site utilization in 2D materials still needs to be improved because the catalytic reduction centers are located in the basal layer and it is difficult for Au(III) to access them. Herein, we demonstrate the application of D-A and D-π-A type one-dimensional (1D) conjugated polymers for the photoenhanced gold recovery from electronic waste. The two synthesized 1D conjugated polymers, named BTP-DET (D-π-A type) and BTP-TDB (D-A type), exhibit a wide light absorption range and excellent chemical stability. Benefiting from the superior charge separation and migration efficiency of the D-π-A skeleton, BTP-DET exhibits a small optical band gap of 1.60 eV, which is significantly smaller than that of BTP-TDB (1.78 eV), and thus enables the extraction of the gold with a higher capacity of BTP-DET (2580.8 mg g−1) under visible light, than the BTP-TDB (2072.7 mg g−1). This work paves the way for exploring low dimensions of conjugated polymers for photocatalysis.

Temperature‐Induced Structural Evolution and Magnetism in Self‐Intercalated V1+xSe2 Nanoplates

Abstract2D van der Waals (vdW) magnets provide the ideal platform for studying magnetism at low dimensions, where the magnetic properties can be further tuned by intercalating magnetic species and manipulating their stoichiometry and local atomic configuration. However, achieving stoichiometric intercalated 2D magnets necessitates precise control of growth parameters to avoid local compositional variations, which fosters phase coexistence that significantly impacts magnetic properties. Here a comprehensive scanning transmission electron microscopy (STEM) examination of the structural evolution in chemical vapor deposition (CVD)‐grown self‐intercalated V1+xSe2 nanoplates is shown, exploring their response to annealing temperature variations and the subsequent influence on magnetic properties. By combining ex situ and in situ post‐growth annealing, the rearrangement of intercalants is directly observed at the interlayer and identify a structural phase transition of the nanodomains embedded in the VSe2 matrix from V3Se4 to a V5Se8 structure occurring at ≈250 °C. The interlayer reconstruction is manifested by a positive‐to‐negative cross‐over in the magnetoresistance and an upturn in resistivity at low temperatures. The results provide new ways for tailoring 2D vdW magnets through post‐growth thermal annealing.

GAN-based self-supervised message passing graph representation learning

Graph representation learning aims to learn embedding representations of nodes in graph-structured data through supervised or unsupervised methods. However, for unlabeled or sparsely labeled graph datasets, conventional supervised approaches fail to perform adequately, and existing unsupervised methods have difficulty achieving high accuracy on multiple downstream tasks such as node classification and link prediction at lower embedding dimensions. To address this issue, we propose a novel self-supervised graph representation learning approach called GAN-based Message Passing Graph Representation Learning. This approach significantly improves the accuracy of node embeddings without relying on label information. Specifically, GMP-GL has developed a generator based on neighbor-weighted random walk to obtain positive and negative node sample pairs, as well as a discriminator that distinguishes whether a real connection exists between node pairs. Through adversarial learning, GMP-GL automatically learns node embeddings. Furthermore, GMP-GL optimizes the training outcomes of the generative adversarial model through self-supervised embedding aggregation learning, thus introducing rich structural information from the graph for the subsequent rounds of generative adversarial training. In addition, GMP-GL employs multiple autoencoders to generate and filter original embeddings, ensuring that the pre-embeddings preserve a significant amount of original feature information for formal learning. The study selects six real-world datasets and conducts comparative experiments with ten SOTA baseline methods. The experimental results demonstrate that the proposed model not only surpasses previous unsupervised methods but also matches or even exceeds the performance of supervised methods. This indicates the great potential of the proposed approach for representation learning tasks on unlabeled graph datasets and its effective applicability to real-world problems.

Material Selection of Conductive Fibers for Touchable Gloves and Application of Graphene

In recent years, nanometer conductive materials are the focus of research. Graphene is widely recognized and used in recent years, they have excellent flexibility, mechanical strength and electrical conductivity, with low dimensions and large specific surface area characteristics, in the field of sensors and daily life has great application potential, perhaps some emerging nanomaterials and technologies can provide the possibility of progress for touch screen gloves. Most touchscreen gloves on the market today use metal fibers, but compared with other metals and non-metallic materials, graphene is undoubtedly a suitable and superior conductive fiber choice now and in the future.