Nonlinear Properties Research Articles

An improved continuum model for hypersonic thermal nonequilibrium flow in the near-continuum regime

In this work, the rarefied Couette flow of diatomic gases with thermal nonequilibrium effects is investigated by the direct simulation Monte Carlo (DSMC) method, and a macroscopic computational model is developed to consider the local rarefaction effects for diatomic gases in the near-continuum regime. The nonlinear transport properties of the diatomic gases are studied, indicating that effective viscosity and effective translational thermal conductivity in the shear nonequilibrium state are affected by translational nonequilibrium effects, which obey the same laws for both monatomic and diatomic gases. The transport coefficients of internal energy modes are affected by both translational nonequilibrium and internal energy relaxation, therefore, the effective rotational and vibrational thermal conductivities are related to the effective viscosity through a modified Eucken relation that accounts for internal energy relaxation. Conclusively, effective constitutive relations are newly established as a function of the shear nonequilibrium parameter and the modified Eucken factors for thermal nonequilibrium flows, and these are integrated into the macroscopic two-temperature model. Subsequently, it is assessed in the simulation of hypersonic flows over flat plates and cylinders at various Knudsen numbers. The results show that the surface shear stress and heat flux obtained by the proposed model agree well with the DSMC results, indicating significantly improved performance compared to the conventional Navier–Stokes two-temperature model for hypersonic flows in the near-continuum regime.

Nonlinear dynamic analysis of aircraft CFRP sandwich wings under explosive blast loading: Introducing SVM-DNN algorithm to predict dynamical information

The aircraft wings are a crucial component, particularly during mechanical shock excitation. Thus, for the first time, an inventive adaptable model to simulate the nonlinear dynamics of the wings under explosive blast loading at the moment of the collision is described in this article. It is crucial to increase the structure's stability because of explosive blast loading. The mentioned structure is made of two composite face sheets and a carbon fiber-reinforced polymer (CFRP). Functionally graded (FG) graphene origami (GOri)-enabled auxetic metallic metamaterial (GOEAM) is introduced to improve the stability of this kind of applicable structure, especially in air maneuvers. The analysis is conducted utilizing Reddy's third-order shear deformation of Reddy theory (TSDRT), incorporating Hamilton's principle and Von Kármán nonlinear geometric assumptions. The governing and boundary equations are accurately computed at the domain and boundary edges of the doubly curved structure. The coupled mesh-free radial point interpolation method (MRPIM) and Newton-Raphson method are applied for numerically simulating the current doubly curved structure by employing Newmark's temporal integration method with a constant-average acceleration approach. The present findings are cross-checked against the machine learning method and open-source results from the literature. Based on physical data, machine learning integrates supervised machine learning to predict the nonlinear vibration of the system. In this case, nonlinear transient bending properties are found using hybrid machine learning methods and solutions. The findings demonstrate the significance of GOEAM for nonlinear vibrations of the composite system. The current study's conclusions could be used as a benchmark and useful recommendations for future structural design techniques with enhanced features.

Coupled flow and energy models with phase change in permafrost from pore- to Darcy scale: Modeling and approximation

We consider coupled models of flow and energy with phase change in thawing soils in permafrost at the pore- and Darcy scales. We develop the underlying models from first principles and define robust and conservative discretization algorithms based on cell-centered finite volume discretization on Cartesian grids and implicit time stepping for individual models. To couple the models we consider sequential approaches as well as time-lagging of some of the nonlinear properties. We also derive practical new surrogate models to obtain Darcy scale data from the pore-scale simulations. Finally, we study the sensitivity of models to the data in realistic scenarios. In particular we find that for the dynamics at the time scale of days, the nonlinear flow parameters have most significant impact on pressure, while temperature is less sensitive to data.

Influence of real gas properties on aerodynamic stability of a SCO2 centrifugal compressor

The safe operation of closed-Bryton-cycle system is hindered by the nonlinear real gas properties in a supercritical carbon dioxide (SCO2) centrifugal compressor for waste heat recovery in a powertrain system. This paper aims to understand the influence of real gas properties on the aerodynamic stability of a shrouded SCO2 centrifugal compressor designed for waste heat recovery. Firstly, the numerical method was calibrated and validated using experimental results from the Sandia SCO2 centrifugal compressor. Next, based on the numerical method, the stability performance of an inhouse-design shrouded SCO2 compressor was discussed through a direct comparison with the identical compressor using air. The results showed that the performance of the SCO2 compressor was significantly different from that of the air compressor. Particularly, the impeller was the most unstable component featuring a notable recirculating region near the shroud at the leading edge. Further analysis is carried out to understand the discrepancies in the stability performance between the two compressors with different fluids. It is revealed that the boundary layer on the SCO2 impeller suction surface thickens at a faster rate, leading to stronger flow separation. Meanwhile, the stronger accumulation of low-momentum secondary flow near the SCO2 impeller outlet enhances the ‘wake’ structure near the shroud of suction surface at the impeller tailing edge, resulting in considerable backflow. The different behaviors of boundary layer were attributed to pressure gradient normal to the suction surface. Specifically, the pressure gradient on the suction surface for SCO2 impeller is orders of magnitude higher than that of the air impeller. The stronger gradient weakens momentum exchange in the boundary layer, thus increasing the thickness of boundary layer more rapidly along the streamwise direction. Moreover, the boundary layer is pushed towards the shroud of suction surface by the strong pressure gradient, resulting in the evident accumulation of secondary flow nearby. At the meantime, the low-momentum flow near the impeller outlet reduced the inlet flow velocity of the diffuser, causing more recirculation at the top of the vaneless diffuser in all circumferential directions, thus worsening its instability.

Electrocardiogram-based Emotion Recognition using Convolutional Neural Networks

Introduction: Electrocardiogram-based facial emotion identification has been a widely spread field in the last few decades. Due to its non-linearity, non-stationary and noisy properties, it is a very difficult job to create a framework that is capable of recognizing emotions with a high recognition rate. background: Electrocardiogram based facial emotion identification has been widely spread field in last some decades. Due to its non-linearity, non-stationary and noisy properties, it is very difficult job to create a framework that is capable of recognizing emotions with high recognition rate. Method: In this work, we introduce a new framework for facial emotion detection based on feature creation using a topographic representation of ECG signal properties. The feature map is created using deep learning techniques, and further, extricated features are then used for classification techniques to detect facial emotion recognition. Result: The recognition results are achieved on two publicly available facial expression datasets, i.e., Ascertain and Dreamer. We illustrated the usefulness of our framework by comparing results with other existing methods. Conclusion: The recognition results prove that the introduced framework can enhance the identifying rate on various given datasets.

Integrated Cost Prediction for Distribution Network Engineering Based on the Novel Multi-objective Coati Algorithm and Laguerre Polynomials

To achieve precise and stable cost prediction for distribution network engineering, a novel multi-objective coati optimization algorithm (MOCOA) is proposed, and based on this algorithm, a new multi-objective integrated prediction model based on Laguerre polynomials (LNN-Adaboost) is developed. Firstly, utilizing the nonlinear approximation properties of Laguerre orthogonal polynomials, a Laguerre neural network is constructed. Secondly, targeting prediction accuracy and stability, the Laguerre neural network is optimized using the multi-objective coati algorithm. Finally, ensemble learning Adaboost is introduced to correct the prediction errors of the model, achieving automatic allocation and recombination of error weights. By comparing with three mainstream multi-objective optimization algorithms on eight test problems, the effectiveness of the proposed algorithm is verified. Taking the cost data of the Ningxia distribution network engineering as the research object, the proposed model MOCOA-LNN-Adaboost is compared with several mainstream models. The results demonstrate that the proposed prediction model exhibits higher prediction accuracy and better prediction stability. Compared to MOCOA-ELM-Adaboost, the proposed prediction model shows an increase of 9.64% in R2, 2.65% in IA, and a decrease of 53.62% in RMSE, and 61.87% in SDEX..

Simulating the LOcal Web (SLOW) II. Properties of local galaxy clusters

This is the second paper in a series presenting the results from a 500 $h^ $Mpc large constrained simulation of the local Universe (SLOW). The initial conditions for this cosmological hydro-dynamical simulation are based on peculiar velocities derived from the CosmicFlows-2 catalog. The simulation follows cooling, star formation, and the evolution of super-massive black holes. This allows one to directly predict observable properties of the intracluster medium (ICM) within galaxy clusters, including X-ray luminosity, temperatures, and the Compton-y signal. Comparing the properties of observed galaxy clusters within the local Universe with the properties of their simulated counterparts enables us to assess the effectiveness of the initial condition constraints in accurately replicating the mildly nonlinear properties of the largest, collapsed objects within the simulation. Based on the combination of several, publicly available surveys we compiled a sample of galaxy clusters within the local Universe, of which we were able to cross-identify 46 of them with an associated counterpart within the SLOW simulation. We then derived the probability of the cross identification based on mass, X-ray luminosity, temperature, and Compton-y by comparing it to a random selection. Our set of 46 cross-identified local Universe clusters contains the 13 most massive clusters from the Planck SZ catalog as well as 70<!PCT!> of clusters with $M_ $ larger than $2 $ M$_ odot $. Compared to previous constrained simulations of the local volume, we found in SLOW a much larger amount of replicated galaxy clusters, where their simulation-based mass prediction falls within the uncertainties of the observational mass estimates. Comparing the median observed and simulated masses of our cross-identified sample allows us to independently deduce a hydrostatic mass bias of $(1-b) approx0.87$. The SLOW constrained simulation of the local Universe faithfully reproduces numerous fundamental characteristics of a sizable number of galaxy clusters within our local neighborhood, opening a new avenue for studying the formation and evolution of a large set of individual galaxy clusters as well as testing our understanding of physical processes governing the ICM.

Exploring the disruptions of the neurophysiological organization in Alzheimer’s disease: An integrative approach

Background and Objective:Alzheimer’s disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. Methods:To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. Results:Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann–Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. Conclusion:This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.

Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm

Precisely forecasting the price of crude oil is challenging due to its fundamental properties of nonlinearity, volatility, and stochasticity. This paper introduces a novel hybrid model, namely, the KV-MFSCBA-G model, within the decomposition–integration paradigm. It combines the mixed-frequency convolutional neural network–bidirectional long short-term memory network-attention mechanism (MFCBA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. The MFCBA and GARCH models are employed to respectively forecast the low-frequency and high-frequency components decomposed through variational mode decomposition optimized by Kullback–Leibler divergence (KL-VMD). The classification of these components is performed using the fuzzy entropy (FE) algorithm. Therefore, this model can fully exploit the advantages of deep learning networks in fitting nonlinearities and traditional econometric models in capturing volatilities. Furthermore, the intelligent optimization algorithm and the low-frequency economic variable are introduced to improve forecasting performance. Specifically, the sparrow search algorithm (SSA) is employed to determine the optimal parameter combination of the MFCBA model, which is incorporated with monthly global economic conditions (GECON) data. The empirical findings of West Texas Intermediate (WTI) and Brent crude oil indicate that the proposed approach outperforms other models in evaluation indicators and statistical tests and has good robustness. This model can assist investors and market regulators in making decisions.

Bioinspired multilayer braided structure with controllable nonlinear mechanical properties for artificial ligaments

Inspired by “crimped” collagen fiber, which induce a low-level load region within the hierarchical structures of ligaments and tendons, this study introduces a braiding technology aiming at characterizing nonlinear mechanical behavior. Samples of the developed artificial ligament with a multilayer braided structure were fabricated and tested. Alterations in the number of braiding layers, wales, and courses significantly affect the mechanical properties of the artificial ligaments. Specifically, the toe length exhibited an increase of up to 104.58 %, while the linear stiffness exhibited variations concomitant with changes in these parameters. Additionally, a mathematical model to describe the relationship between the toe length and linear stiffness was developed based on the braiding parameters and material properties of the fibers. Alternative fibers were employed and tested to demonstrate a consistency with the mathematical model. The correlation coefficients were calculated as 0.9644 for the toe length and 0.9203 for the linear stiffness. Lastly, this study mimics the mechanical behavior of the human medial collateral ligament (MCL) by integrating the artificial ligament into a bio-fidelity knee model with a joint stiffness of 1.77 Nm/deg, resulting in a high degree of similarity with human knee in valgus-varus, further affirming the utility and applicability of this study.

Gas-Spring Quasi-Zero Stiffness Air Damping Isolator for Vertical Vibration Control of Indoor Substation Excited by Metro Loads

To mitigate the metro-induced vertical vibration of the indoor substation structure, this study proposes a gas-spring quasi-zero stiffness air damping isolator (AD-QZSI) with excellent low dynamic stiffness and high-static stiffness characteristics. The working principle and mechanical properties of the AD-QZSI are introduced and studied through theoretical and numerical methods. A model for substation considering soil–structure–equipment interaction is established using the software ABAQUS, its accuracy is validated based on a series of measured data from actual projects, and the AD-QZSI’s simulation method and parameter design method are described in detail. The air damper’s stiffness [Formula: see text] is integrated into the isolator’s mechanical model, theoretically and numerically achieving an accurate simulation of AD-QZSI’s nonlinear mechanical properties. The numerical results have an error of less than 5% with the measured data, indicating that the model is able to better capture the actual structure’s dynamic characteristics and is reasonable to be employed for subsequent analysis. Numerical results show that AD-QZSI can significantly reduce the structural vertical vibration, and its control effect is better in the whole frequency band, in particular, the effect is also visible in the low-frequency band, indicating that its vibration isolation frequency band is wider than that of traditional QZS isolator. With the vibration source distance increasing, the control effect of AD-QZSI presents a tendency to decrease and then level off, and its vibration isolation gain is weakened by the continuous increase of the damping ratio greater than 0.01. Moreover, the equipment’s dynamic amplification factor of the isolated structure decreases significantly. Finally, the proposed AD-QZSI can obtain ideal quasi-zero stiffness characteristics by adjusting the air pressure, and the adopted air damper belongs to the green low-carbon components, featuring great practical value and application prospects.

Soliton mode-locked pulse generation with Polyaniline saturable absorber

We demonstrate the successful mode-locking of an Erbium-doped fiber laser (EDFL) utilizing a Polyaniline (PANI) saturable absorber (SA), resulting in the production of picosecond pulses at a frequency of 1.8 MHz within an elongated ring cavity. The SA is engineered by embedding PANI into Polyvinyl Alcohol (PVA). This conducting polymer-based SA exhibits favorable optical nonlinear properties, boasting a modulation depth of 2.3 %, a saturation intensity of 7.8 MW/cm2, and a non-saturable loss of 5.6 %. The laser system generates a conventional soliton pulse train at a central wavelength of 1572.62 nm. The mode-locking operation is sustained within a pump power range from 139.4 to 254.12 mW. The laser also achieves a pulse duration of 3.0 ps and a maximum pulse energy of 0.4 nJ. These findings underscore the effectiveness of the proposed PANI-based SAs, which exhibit comparable properties to currently utilized 2D SAs. The successful mode-locking operation and the promising optical characteristics of the PANI-based SA provide a solid foundation for their potential applications in the realm of ultrafast photonics.

Revealing photoluminescence and nonlinear optical absorption characteristics of PbMo0.75W0.25O4 single crystal for optical limiting applications

Nonlinear absorption properties of PbMo0.75W0.25O4 single crystal fabricated by the Czochralski method were studied. The band gap energy of the crystal was determined as 3.12 eV. Urbach energy which represents the defect states inside the band gap was found to be 0.106 eV. PbMo0.75W0.25O4 single crystal has a broad photoluminescence emission band between 376 and 700 nm, with the highest emission intensity occurring at 486 nm and the lowest intensity peak at 547 nm, depending on the defect states. Femtosecond transient absorption measurements reveal that the lifetime of localized defect states is found to be higher than the 4 ns pulse duration. Open aperture (OA) Z-scan results demonstrate that the PbMo0.75W0.25O4 single crystal exhibits nonlinear absorption (NA) that includes two-photon absorption (TPA) as the dominant mechanism at the 532 nm excitations corresponding to 2.32 eV energy. NA coefficient (β eff ) increased from 7.24 × 10−10 m W−1 to 8.81 × 10−10 m W−1 with increasing pump intensity. At higher intensities β eff tends to decrease with intensity increase. This decrease is an indication that saturable absorption (SA) occurred along with the TPA, called saturation of TPA. The lifetime of the defect states was measured by femtosecond transient absorption spectroscopy. Saturable absorption behavior was observed due to the long lifetime of the localized defect states. Closed aperture (CA) Z-scan trace shows the sign of a nonlinear refractive index. The optical limiting threshold of PbMo0.75W0.25O4 single crystal at the lowest intensity was determined as 3.45 mJ/cm2. Results show that the PbMo0.75W0.25O4 single crystal can be a suitable semiconductor material for optical limiting applications in the visible region.

Rapid formation of imidazolium glutarate (IMGA) compound and investigation of its single crystal growth by modified vertical Bridgman technique for optical limiting applications

In this paper, we have examined the formation of the imidazole glutarate adduct by protonation-deprotonation between imidazole and glutaric acid using a direct precipitation method within a short period. Following that, we have succeeded in growing imidazolium glutarate (IMGA) single crystaldirectly from synthesized material and we have also grown IMGA single crystal from the melt methodusing the vertical Bridgman technique for the first time in literature. A special Bridgman furnace was constructed with a slight modification in the winding section for this crystal growth. Crystal structure, phase purity, and growth plane were analyzed for the grown crystal from both single and powder XRD analysis. Functional groups were verified after the growth using FTIR transmittance analysis. The optical homogeneity and growth mechanism were scrutinized by birefringence interferogram and chemical etching studies, respectively. The grown IMGA has 80 % transparency in the entire visible region. The cut-off wavelength of the grown crystal is 235 nm, and the optical band gap energy of IMGA was found to be 5.26 eV. The photocurrent study reveals that IMGA has a negative photoconductivity nature. Third-order nonlinear parameters were found by the Z-scan technique. IMGA has a self-defocusing nature, negative non-linearity, and saturated absorption properties. The calculated χ(3) value is 2.045 x 10-8 esu.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device’s superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

A generic design motif for metamaterials with controllable nonlinearity & guided deformation

Structure design motifs take an essential role in creating materials or metamaterials with superior mechanics or special functions. However, a fundamental question naturally arising and remaining unknown is related to how to effectively design an engineered material structure with “adjustable and predictable” nonlinear mechanic properties conveniently. Here we propose a very simple but versatile design motif, named as bq-structure, which can provide superior flexibility in achieving various nonlinear properties, including 200 % larger and designable ranges of quasi-zero stiffness, negative stiffness, and multi-stable stiffness, etc, that original material or traditional beam-based design motifs cannot provide. The proposed design motif has sufficient reliability in nonlinear manipulation with a “guided & controllable” manner instead of “trail-and-error attempts” existing in most existing ones. A prototyped vibration isolation unit can achieve a resonant frequency less than 1 Hz easily which cannot be done with many other methods of similar size and payload. Given the large elastic deforming capacity and ductility in structural scale, the superb nonlinearity regime is generally designable with various structure parameters, uniquely providing a superior optimization tool with this novel design motif considering various real application requirements for advanced materials design, energy storage or conversion, mechanical vibration mitigation, and robotic systems etc.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties

Abstract In this study, cobalt nanoparticles (CoNPs) were prepared by using an Nd–YAG pulsed laser at a wavelength of 1,064 nm and an energy of 800 mJ. From the results of the structural and morphological attributes of CoNP films, it is evident that CoNPs exhibit different spherical configurations with diameters spanning from a few nanometers to 24 nm. The surfaces of these CoNP films indicate that they exhibit a certain degree of homogeneity and uniform vertical heights. Additionally, the results demonstrate an increase in granular size growth and the emptying of cavities on the material’s surface. The absorption curves appeared as a function of the wavelength within the range of 300–1,000 nm and the peak absorption of CoNPs in the ultraviolet radiation region, and the prohibited energy gap for direct transitions was known, and the value is 4 eV, while the nonlinear optical properties showed that the particles show a nonlinear refractive index (self-defocusing) and a nonlinear absorption cobalt coefficient with the absorption of two photons; thus, the CoNPs possess good nonlinear properties and can be used in nonlinear optical photonic device, optical power limiter, and a wide range of nonlinear applications.

Advanced Functionalized Nanoclusters (Cu, Ag, and Au) as Effective Catalyst for Organic Transformation Reactions.

A considerable amount of research has been carried out in recent years on synthesizing metal nanoclusters (NCs), which have wide applications in the field of optical materials with non-linear properties, bio-sensing, and catalysis. Aside from being structurally accurate, the atomically precise NCs possess well-defined compositions due to significant tailoring, both at the surface and the core, for certain functionalities. To illustrate the importance of atomically precise metal NCs for catalytic processes, this review emphasizes 1) the recent work on Cu, Ag, and Au NCs with their synthesis, 2) the parameters affecting the activity and selectivity of NCs catalysis, and 3) the discussion on the catalytic potential of these metal NCs. Additionally, metal NCs will facilitate the design of extremely active and selective catalysts for significant reactions by elucidating catalytic mechanisms at the atomic and molecular levels. Future advancements in the science of catalysis are expected to come from the potential to design NCs catalysts at the atomic level.

Self-Cross-Linking Latex Pressure-Sensitive Adhesives: Preparation and Investigation on Adhesion, Linear Viscoelastic, and Nonlinear Large-Strain Properties

Self-Cross-Linking Latex Pressure-Sensitive Adhesives: Preparation and Investigation on Adhesion, Linear Viscoelastic, and Nonlinear Large-Strain Properties