Performance Advantages Research Articles

Electronically Conductive Polymer Enhanced Solid-State Polymer Electrolytes for All-Solid-State Lithium Batteries

All-solid-state lithium batteries (ASSLBs) have gained enormous interest due to their potential high energy density, high performance, and inherent safety characteristics for advanced energy storage systems. Although solid-state ceramic (inorganic) electrolytes (SSCEs) have high ionic conductivity and high electrochemical stability, they experience some significant drawbacks, such as poor electrolyte/electrode interfacial properties and poor mechanical characteristics (brittle, fragile), which can hinder their adoption for commercialization. Typically, SSCE-based ASSLBs require high cell stack pressures exerted by heavy fixtures for regular operation, which can reduce the energy density of the overall battery packages. Polymer–SSCE composite electrolytes can provide inherently good interfacial contacts with the electrodes that do not require high cell stack pressures. In this study, we explore the feasibility of incorporating an electronically and ionically conducting polymer, polypyrrole (PPy), into a polymer backbone, polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP), to improve the ionic conductivity of the resultant polymer–SSCE composite electrolyte (SSPE). The electronically conductive polymer-incorporated composite electrolyte showed superior room temperature ionic conductivity and electrochemical performance compared to the baseline sample (without PPy). The PPy-incorporated polymer electrolyte demonstrated a high resilience to high temperature operation compared with the liquid-electrolyte counterpart. This performance advantage can potentially be employed in ASSLBs that operate at high temperatures. In our recent development efforts, SSPEs with optimal formulations showed room temperature ionic conductivity of 2.5 × 10−4 S/cm. The data also showed, consistently, that incorporating PPy into the polymer backbone helped boost the ionic conductivity with various SSPE formulations, consistent with the current study. Electrochemical performance of ASSLBs with the optimized SSPEs will be presented in a separate publication. The current exploratory study has shown the feasibility and benefits of the novel approach as a promising method for the research and development of next-generation solid composite electrolyte-based ASSLBs.

Multi-Scale Depthwise Separable Capsule Network for hyperspectral image classification.

Addressing the challenges in effectively extracting multi-scale features and preserving pose information during hyperspectral image (HSI) classification, a Multi-Scale Depthwise Separable Capsule Network (MDSC-Net) is proposed in this article for HSI classification. Initially, hierarchical features are extracted by MDSC-Net through the employment of parallel multi-scale convolutional kernels, while computational complexity is reduced via depthwise separable convolutions, thus reducing the overall computational load and achieving efficient feature extraction. Subsequently, to enhance the translational invariance of features and reduce the loss of pose information, features of various scales are processed in parallel by independent capsule networks, with improvements in max pooling achieved through dynamic routing. Lastly, features of different scales are concatenated and integrated through the concatenate operation, thereby facilitating precise analysis of multi-level information in the hyperspectral image classification process. Experimental comparisons demonstrate that MDSC-Net achieves average accuracies of 94%, 98%, and 99% on the Kennedy Space Center, University of Pavia, and Salinas datasets, respectively, indicating a significant performance advantage over recent HSI classification models and validating the effectiveness of the proposed model.

Nanocages encapsulated Ni-W alloy nanoreactor for valorization of lignin derivatives

Nanoscale hollow alloy reactor has excellent performance advantages in the hydrodeoxygenation (HDO) of lignin derivatives, but their facile synthesis remains a challenge. Herein, a smart one-pot hydrothermal etching strategy was developed, and the obtained Ni-W alloy hollow nanoreactor (NiW0.5@NCs) exhibits efficient HDO performance for vanillin (VL) (100 % yield of 2-methoxy-4-methylphenol (MMP) under 110 °C). Theoretic calculations and experimental results indicated that the W species optimized the d band center structure through the formation Ni-W alloy sites, regulated the acidity of the catalyst, and thus facilitated the selective adsorption/activation of C=O and the cleavage of C-O bond, resulting in an efficient HDO of VL to MMP. Furthermore, the NiW0.5@NCs catalyst showed robust stability and substrate universality, which confirms this research provides a feasible route for the constructing efficient nanoscale hollow reactor for hydrodeoxygenation of lignin derivatives.

Performance and cost advantage of stocking locally sourced Walleye fry into southern Minnesota lakes

AbstractObjectiveA genetically distinct strain of Walleye Sander vitreus (southern Minnesota strain [SMS]) persists in southern Minnesota lakes despite decades of stocking more easily obtained strains from outside of the region. Because of the regional performance advantage inferred by this persistence, we conducted in situ experiments to compare survival and stocking cost of SMS Walleye fry against those of a frequently stocked northern Minnesota strain (Mississippi headwaters strain [MIS]) to determine whether it is beneficial to expand SMS fry stocking in lieu of historic practices.MethodsWe conducted four paired stockings of oxytetracycline‐marked SMS fry and MIS fry into six southern Minnesota lakes and then sampled the fish as fall fingerlings during 2018, 2019, 2021, and 2022. We then used fluorescent microscopy and parentage‐based tagging techniques to discern the stocked strains for comparison of growth and survival. We also obtained production and stocking cost data from agency records to calculate relative stocking costs.ResultBy the end of their first summer, SMS Walleye from all but two stockings exhibited higher survival than MIS Walleye. Despite higher initial costs to produce SMS fry, their higher survival to fall fingerlings made the stocking of SMS fry more cost effective than MIS fry stocking. Natural reproduction was also identified in several stocked lakes, most of which consisted of either high or increasing levels of SMS ancestry.ConclusionA local strain can outperform nonlocal strains at a level that can overcome additional costs of developing a new egg source. In addition, natural reproduction of local‐strain Walleye may reduce or eliminate the need for future stocking.

Proposal and comparison of two heat recovery measures for the coal-based Allam cycle: Double expansion and lower turbine backpressure

Improving the thermodynamic efficiency of the coal-fired semi-closed supercritical CO2 cycle (coal-based Allam cycle) can be achieved by implementing self-heat recuperative coal drying and recompression with more syngas heat integrated with the efficient power cycle. To prevent the heat exchanger from overtemperature during syngas heat recovery, two measures are considered: double expansion and lower turbine backpressure. For the double expansion, a portion of the recycle CO2 is fed into an expander and the exhaust is mixed with the flue gas in the main recuperator to raise the recycle flow rate. For the lower turbine backpressure, the turbine backpressure is decreased to reduce exhaust flue gas temperature. Thermodynamic analysis indicated that self-heat recuperative coal drying and recompression could enhance the net efficiency by 4.28 % points compared to the basic cycle, but the heat exchanger for raw syngas heat recovery faces overtemperature problem with the cold stream exceeding 777 °C. Both the measures proposed above can effectively reduce the temperature to 730–760 °C with slightly decreased efficiencies of 46.93 %∼47.68 % for double expansion and 47.12 %∼47.81 % for lower turbine backpressure. Furthermore, economic assessment shows that compared to the basic cycle, the cost of electricity decreases by 2.49 % and 5.04 % for the schemes with double expansion and lower turbine backpressure, respectively. Therefore, the proposed design with lower turbine backpressure demonstrates advantages in both thermodynamic and economic performance.

A study on preparation of low-cost environment-friendly self-assembled nanocellulose membrane from waste paper

In the context of green development, different types of waste paper were used in this study to prepare environment-friendly nanocellulose membrane by using different pretreatment processes. The results showed that all nanocellulose membranes had a novel microscopic composite morphology: nanocellulose fibers in the middle were tightly arranged and had a certain direction, while nanocellulose fibers at the edge were arranged randomly. Moreover, the type of waste paper affected the formation and property of nanocellulose membranes deeply compared with the pretreatment process. Nanocellulose membrane prepared from office waste paper had a high transmittance (50.8 %) and low oxygen transmission rate (1.548·10−3/cm3mm/(m2·d·KPa)) while that prepared from waste corrugated paper had a high yield (50.63 %), high folding number (81 times), and low water vapor transmission rate (1550.24 g·m−2·day−1). Meanwhile, nanocellulose membrane prepared without any deinking treatment had a significant performance advantage in terms of water vapor barrier and folding resistance. Therefore, in actual application, deinking treatment can be selectivity omitted, which will simplify the preparation process flow and reduce the production cost of nanocellulose membrane. Furthermore, in order to satisfy different application requirements, the appropriate raw materials and treatment processes can be chosen to prepare nanocellulose membranes with different properties. This study provides a simple and low-cost way to prepare high-performance environment-friendly self-assembled nano membrane material, and also achieves the high value utilization of waste paper, which has a certain ecological, economic, and social benefits.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

Thermodynamic and mass analysis of a novel two-phase liquid metal MHD enhanced energy conversion system for space nuclear power source

The higher-efficiency and more lightweight space nuclear power source is one of the most important prerequisites for future deep space exploration. Magnetohydrodynamic (MHD) power generation is the potential technical route to achieve significant improvement in energy conversion performance, but technology is not yet mature. By combining the characteristics of MHD and closed Brayton cycle (CBC) power generation systems, a novel quasi-Ericsson cycle with performance advantages is proposed in this study. The estimation method for the MHD power generator is refined and the thermodynamic and mass analysis models for the novel system are established. Comparative studies between the LMMHD-CBC cycle and the traditional CBC cycle are conducted under various boundary conditions and internal parameters. The results indicate that the LMMHD-CBC cycle increases the electricity generation per unit mass flow rate of working fluid under the same temperature conditions, thereby achieving a reduction in the specific mass of the power generation system. The LMMHD-CBC system is more suitable in lower reactor temperatures and higher radiator temperature conditions. Within the parameters range considered in this study, the relative reduction in specific mass compared to traditional CBC systems reaches up to 30.84 %.

Research on numerical modeling of two-dimensional freak waves and prediction of freak wave heights based on LSTM deep learning networks

Freak waves have posed a serious threat to the safety of marine structures. Thus, the accurate simulation and prediction of freak waves are crucial for maritime safety. This paper proposes a nonlinear numerical method based on phase modulation. This method achieves precise time and location simulation of two-dimensional freak waves, while preserving the key statistical characteristics of the wave sequence, thereby obtaining more accurate two-dimensional wave height information. Additionally, this paper constructs a Long Short-Term Memory (LSTM) deep neural network integrated with a Sequence-to-Sequence (seq2seq) framework for predicting sequences of freak wave heights. In this study, we conduct an in-depth predictive analysis of the experimental data for sequences of freak wave heights. And this paper provides a comprehensive assessment of the performance of Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks in the task of freak wave prediction. The experimental results indicate that the seq2seq-LSTM model demonstrates significant performance advantages in predicting the wave heights of freak waves, particularly for long-term predictions. In summary, the numerical simulation method based on phase modulation and the improved seq2seq-LSTM deep learning model are of significant practical value for enhancing the safety of offshore platforms and ships.

A Four-Dimensional No-Equilibrium Chaotic System with Multi-Scroll Chaotic Hidden Attractors and its Application in Image Encryption

Abstract In recent years, constructing hidden attractors with multi-scroll has become a key discussion point in the research and application fields of chaos science. In this paper, with the existing four-dimensional (4D) chaotic system as the base, a new four-dimensional chaotic system featuring significant characteristics of multi-scroll hidden attractors is constructed by adding a nonlinear function. Comprehensive studies including theoretical analyses and numerical simulations have been carried out on the dynamic properties of the new chaotic system, and all the results show that this system exhibits extremely complex chaotic behaviours and excellent unpredictability, which has great value in image encryption. Therefore, an image encryption scheme based on the new chaotic system is proposed, which cleverly integrates the new scrambling algorithm based on parity coordinate transformation and the new rotational diffusion algorithm. And the effectiveness of this encryption algorithm has been thoroughly analyzed and tested. The results based on the experiments show that this encryption algorithm exhibits significant advantages in performance, which can greatly enhance the security of images during encryption and transmission.

A lightweight anonymity protocol at terabit speeds on programmable switches

Anonymous communication protocols are integral tools for people to exercise the right to privacy in their online activities. Such tools are urged to offer maximum protection against various classes of adversaries, including rogue criminals, exploitative companies, and authoritarian or paternalistic regimes, while respecting the diversity of users’ activities, from e-mail and web browsing to file transfer and video streaming. In such a context, lightweight anonymity protocols are a promising solution to provide well-balanced anonymity and performance for daily use against a class of adversaries with a relaxed but realistic assumption of their capability. However, as the existing instantiations of these protocols solely target software implementation, they did not fully exhibit their advantages in performance by the relaxation of the adversarial model. This paper presents P5HI, a novel instantiation and implementation of a lightweight anonymity protocol on hardware programmable switches. Its realization requires a rethinking of all aspects of the system, beginning with the realization of bit-rotation on the switch, through the construction of a cryptographic scheme and attack prevention, to hardware-friendly procedures during path setup and data transmission. We deploy the full-fledged design of the lightweight anonymity protocol on an actual programmable switch, and our prototype demonstrates its operation at a speed of over 3.0 Tbps.

Fabrication of compact substrate integrated waveguide dual band bandpass filter using complementary split-ring resonators for C and X band applications

This article presents a compact dual-band Bandpass Filter(BPF) based substrate integrated waveguide(SIW) used for C and X band applications. Complementary split-ring resonators(CSRR) are loaded into the waveguide surface to obtain more advantages of compactness and good performance. The SIW cavity undergoes initial optimization utilizing the powerful genetic algorithm, renowned for its robustness in solving complex problems, before proceeding to simulation with Ansys HFSS software with loaded CSRRs. The filter displays two passbands: the first has a bandwidth of 2 GHz and resonates at 6.8 GHz, while the second has a bandwidth of 5,55 GHz and resonates at 10.3 GHz. Moreover, with two resonance frequencies, the developed BPF has the benefit of low insertion loss(S21=-1.7dB and -1.9 dB) and improved return loss: (S11=-24dB and -22 dB) respectively. Furthermore, the proposed combination not only allows the implementation of a forward-wave passband propagating below the characteristic cutoff frequency of the waveguide but also exhibits good rejection, demonstrated by the presence of two transmission zeros with rejection of -16 dB and -19 dB, attained at 7.4 GHz and 14.8 GHz respectively. The proposed SIW BPF was designed using a 0.6mm thick FR4 dielectric substrate measuring 13mm × 15mm. The filter is manufactured to validate the concept and the measured results agree well with the simulation. With all these advantages: compact size, high rejection, and good filtering response, the proposed SIW BPF became suitable for microwave applications.

Service Quality as a Catalyst for Competitive Advantage and Business Performance in Hotel Industry: An Empirical Analysis by PLS-SEM Algorithm

This study investigates the vital role of service quality in the hotel industry, focusing on its impact on business performance and competitive advantages. Utilizing cross-sectional survey responses from hotel employees, we employed partial least squares structural equation modelling to analyze the relationships between service quality, competitive advantage, and business performance. Results highlight the pivotal role of quality in enhancing competitive advantages and operational efficiency in the hotel sector. The findings underscore the significant influence of service quality on both competitive advantage and overall business performance in the tourism service sector, leading to positive outcomes such as improved tourist experiences, economic growth, and an enhanced destination image. Additionally, the study emphasizes the importance of competitive advantage, service quality, and innovation in driving business performance, with direct implications for pricing strategies and service quality in the hotel industry.

Wushu Movement Recognition System Based on DTW Attitude Matching Algorithm

Motion recognition technology is widely used in intelligent video surveillance, human-computer interaction and other fields. With the development of computer vision technology, improving the accuracy and efficiency of motion recognition has become the focus of research. The purpose of this study is to improve the performance of Wushu movement recognition through improved dynamic time warping algorithm and hierarchical model. Firstly, a high-dimensional feature vector is constructed by using the position, velocity and Angle changes of human bone joints. The actions are subdivided by the hierarchical model, and matched and recognized by the max-minimum dynamic time regularization model. Meanwhile, the K-class mean algorithm is combined to optimize the type of tree core, improve the performance of the model, reduce the interference of noise nodes, and effectively classify Wushu actions. Experimental verification was carried out on four public data sets of KTH, Olympic Sports, Hollywood2 and HMDB51. The experimental results showed that the recognition rate of the proposed model in KTH data set was 95.2%, and that in Olympic Sports data set was 91.4%. The Hollywood2 dataset was 66.7%, and the HMDB51 dataset was 61.2%. Comparing the results of different algorithms, the proposed method improved the recognition performance by 10% compared with long short-term memory network and gated cycle unit. Compared with one-dimensional convolutional neural network, the time of the proposed method was 15s longer, but the recognition rate was 1.6% higher. The results showed that the proposed method had significant performance advantages in diverse and complex action recognition tasks. Meanwhile, the results emphasized the factors to be considered in the design of the model, demonstrating its effectiveness in the application of Wushu movement recognition.

Wearable damaged clothing fabric image recognition system based on image restoration algorithm

With the improvement of people's living standards and the rapid growth of national technological strength, wearable devices have gradually integrated into people's daily lives, making them convenient for people's work and lives. Traditional fabric detection methods often rely on manual visual inspection, which has low efficiency and accuracy that does not meet the requirements. In view of this, the research has innovatively integrated GAN, CNN, field Programmable gate array (FPGA), multi-stage image recovery algorithm based on graph neural network, for high frequency and low frequency fusion, and combined it with wearable devices to develop a new wearable fabric recovery and recognition system. The results showed that in the COCO dataset, after 250 iterations, the WFR2S algorithm achieved a damage identification accuracy of 95.5 %, a mean absolute error of 0.011, a real-time processing speed of 30 fps, and a resource consumption of 2.4 W. These data demonstrate the significant performance advantages of WFR2S in image recovery and damage detection. The newly developed image recovery algorithm improves the accuracy, availability and stability of damage recognition in garment fabrics. This study not only has theoretical value but also has broad application prospects in practical application. The aim is to contribute to the intelligent development of the garment industry.

Synergistic effects of zinc thiocyanate on the performance of cesium lead bromide perovskite phosphors for solid-state lighting applications

All inorganic lead halide perovskites have led to a new paradigm shift in display and lighting technologies because of significant cost and performance advantage over the current state-of-the-art optoelectronics. However, the applications of these perovskites are undermined due to the toxicity and severe degradation of the materials in response to external stimuli. This research highlights the synergetic effects of co-doping of CsPbBr3 nanocrystals (NCs) at its B- and X-site using zinc thiocyanate (Zn(SCN)2). The adopted doping strategy helps to improve the photoluminescence stability of the pristine NCs when stored under ambient conditions for 156 days. This is attributed to the enhanced local structural order and reduced inhomogeneous strain between the lattices of the NCs due to the synchronized effect of Zn2+ and SCN–. The composite NCs are integrated as color-converting layers (thin 3D printing layer coated with NCs) with a blue LED chip to obtain the white light. It exhibits bright white emissions with a color rendering index of 86, correlated color temperature of 6046 K, and color coordinates close to standard white light. This work emphasizes the strong potential of inexpensive and earth-abundant Zn(SCN)2-based CsPbBr3 composites to meet consumer needs.

Influence of AM Generated Burner Surface Roughness on NOx Emissions and Operability of Hydrogen-Rich Fuels

ABSTRACT The additive manufacturing (AM) technique enables the fabrication of advanced burner components to enhance the hydrogen capability of the existing gas turbines (GTs) and reduce the carbon footprints of the power generation sector. This technique produces rough surfaces that may require post-processing to maintain the desired functionality of a burner, particularly for hydrogen fuel with unique thermo-physical properties. This study, therefore, compared the stability of three swirlers of variable surface roughness manufactured using AM and traditional machining methods, with one of the AM swirler post-processed by grit-blasting. The comparison included a conventional benchmark (100% CH4), low carbon (23%volCH4/77%volH2) and zero carbon (100% H2) fuels across a range of equivalence ratios. Additionally, the study quantified the flame topology and emissions performance of the fuel blends for each swirler using high-speed OH* chemiluminescence and exhaust gas emissions measurements, respectively. The experimental investigation concluded that the AM-generated surface roughness within the considered range does not detrimentally impact NOX emissions and the stability of the fuel mix. However, the flame location was observed to be influenced by surface roughness and shifted more toward the vertical centerline of the burner with increased roughness. From the practical perspective, the results showed that post-manufacturing surface finishing offers negligible performance advantages, indicating potential cost reductions. It is recommended that further studies should investigate the influence of increased surface roughness on burner performance, as well as numerical modeling techniques which could provide an insight into when AM surfaces are likely to be more influential.

Numerical investigation on heat transfer and pressure drop characteristics of Micro-Pillar array surface

The array surface structure can improve heat transfer efficiency and achieve large heat transfer area per unit of volume. In this study, a numerical simulation using CFD method was carried out to construct a dual-channel three-dimensional mathematical model with micro-pillar array surface. The characteristic of heat transfer and flow resistance for the arrayed surface structure have been studied. In addition, four different array configurations, including cylindrical, elliptical, quadrangular and herringbone, were considered to compare the thermal performance and flow behavior. The configuration with the best overall performance was obtained as cylindrical by comparison. The effects of different heights, arrangement methods and pitches on the system performance were analyzed. The velocity distribution on the array surface was also obtained. It is found that the cylindrical array structure has obvious advantages in overall performance, with more uniform surface velocity distribution and better overall performance at lower Re conditions.

Study on the synergistic regeneration effect of tall oil and crumb rubber on aged SBS modified asphalt

The thermo-oxidative aging of Styrene-Butadiene-Styrene (SBS) modified asphalt (SMA) includes the aging of base asphalt and the degradation of SBS modifiers. With the aim of restoring the performance of base asphalt and making up for the loss of performance advantage caused by the degradation of SBS modifier, tall oil and crumb rubber (CR) were applied to synergistically regenerate aged SBS modified asphalt (ASMA). The effect of the particle size and content of CR, as well as the degree of microwave-activated CR on the properties and storage stability of the reclaimed ASMA were discussed in details. The regeneration mechanism of ASMA by CR was studied from the surface properties, morphology and thermal behavior, respectively. The results show that tall oil can restore the low-temperature properties of ASMA significantly, but it seriously detrimental to the high-temperature properties. CR play the role of secondary modification on the reclaimed ASMA, which can improve the high-temperature properties of the reclaimed ASMA at the cost of low-temperature properties and storage stability, and the modification effect is related to the particle size and content of CR. Microwave activation of CR can improve the storage stability of the reclaimed ASMA with CR, which is because that microwave activation modification disrupts the cross-linking structure of the CR and improves the surface properties, which are conducive to the diffusion migration of molecular chains and multiphase material exchange between the CR and the reclaimed ASMA. Thus, the interaction between the multiphases can be enhanced in the reclaimed ASMA with CR. This work provides an effective regeneration method for aged SBS modified asphalt, and also achieves the reuse of non-renewable resources.

Comparative Evaluation of Commercial Test Kits Cleared for Use in Modified Two-Tiered Testing Algorithms for Serodiagnosis of Lyme Disease.

Modified 2-tiered testing (MTTT) for Lyme disease utilizes automatable, high throughput immunoassays (AHTIs) in both tiers without involving western immunoblots, offering performance and practical advantages over standard 2-tiered testing (STTT; first-tier AHTI followed by immunoglobulin M (IgM) and immunoglobulin G (IgG) western immunoblots). For MTTT, Centers for Disease Control and Prevention recommends using AHTI test kits that have been cleared by Food and Drug Administration (FDA) specifically for this intended use. We evaluated performance of FDA-cleared MTTT commercial test kits from 3 manufacturers by comparing with STTT results. We performed MTTT (total antibody AHTI with reflex to separate IgM and IgG AHTIs) using test kits from Diasorin, Gold Standard Diagnostics (GSD), and Zeus Scientific on 382 excess serum samples submitted to the clinical laboratory for routine Lyme disease serologic testing in July 2018, measuring agreement between MTTT and STTT using the κ statistic. Overall agreement with STTT was 0.87 (95% confidence interval [CI], .77-.97) using Diasorin assays (almost perfect agreement), 0.80 (95% CI, .68-.93) using GSD assays (substantial agreement) and 0.79 (95% CI, .68-.90) using Zeus assays (substantial agreement). For detection of IgM reactivity, agreement between MTTT and STTT was 0.70 (.51-.90; substantial), 0.63 (95% CI, .44-.82; substantial) and 0.56 (95% CI, .38-.73; moderate), respectively. For detection of IgG reactivity, MTTT/STTT agreement was 0.73 (95% CI,.58-.88), 0.78 (95% CI, .62-.94), and 0.75 (95% CI, .60-.90), respectively (substantial agreement in all cases). MTTT results obtained using commercial test kits from 3 different manufacturers had substantial to almost perfect agreement with STTT results overall and moderate to substantial agreement for IgM and IgG detection independently. Commercial MTTT tests can be used broadly for the diagnosis of Lyme disease.