Electrostatic Gating of Spin Dynamics of a Quasi-2D Kagome Magnet.

Electrostatic gating has emerged as a powerful technique for tailoring the magnetic properties of two-dimensional (2D) magnets, offering exciting prospects including enhancement of magnetic anisotropy, boosting Curie temperature, and strengthening exchange coupling effects. Here, we focus on electrical control of the ferromagnetic resonance of the quasi-2D Kagome magnet Cu(1,3-bdc). By harnessing an electrostatic field through ionic liquid gating, significant shifts are observed in the ferromagnetic resonance field in both out-of-plane and in-plane measurements. Moreover, the effective magnetization and gyromagnetic ratios display voltage-dependent variations. A closer examination reveals that the voltage-induced changes can modulate magnetocrystalline anisotropy by several hundred gauss, while the impact on orbital magnetization remains relatively subtle. Density functional theory (DFT) calculations reveal varying d-orbital hybridizations at different voltages. This research unveils intricate physics within the Kagome lattice magnet and further underscores the potential of electrostatic manipulation in steering magnetism with promising implications for the development of spintronic devices.

ZnS:Mn Quantum Dots Coated with a Silica Molecularly Imprinted Polymer for Trace Teflubenzuron Detection in Vegetable Samples.

A novel nanocomposite fluorescent probe consisting of quantum dots and a silica molecularly imprinted polymer (MIPs-capped ZnS:Mn QDs) was synthesized and applied for the rapid detection of teflubenzuron (TBZ) based on the fluorescence quenching of a composite probe via TBZ. The fluorescence quenching efficiency of MIP@SiO2@ZnS:Mn QDs displayed a linear relationship over the concentration range of 0-26.24 µmol/L with a correlation coefficient of 0.9857 and the limit of detection was 2.4 µg/L. The selectivity test showed that the nanocomposite had good selectively rebind TBZ with higher imprinting factor of 3.06 compared with four structurally similar compounds. In addition, the probe was successfully applied to the detection of TBZ in vegetable samples with a recovery of 90.3~97.1% and with a relative standard deviation below 3.2%. This developed method has the advantages of simple preparation, fast response and low toxicity for trace TBZ detection.

Meta-analysis of 16S rRNA microbial data identified alterations of the gut microbiota in COVID-19 patients during the acute and recovery phases.

BACKGROUND: Dozens of studies have demonstrated gut dysbiosis in COVID-19 patients during the acute and recovery phases. However, a consensus on the specific COVID-19 associated bacteria is missing. In this study, we performed a meta-analysis to explore whether robust and reproducible alterations in the gut microbiota of COVID-19 patients exist across different populations. METHODS: A systematic review was conducted for studies published prior to May 2022 in electronic databases. After review, we included 16 studies that comparing the gut microbiota in COVID-19 patients to those of controls. The 16S rRNA sequence data of these studies were then re-analyzed using a standardized workflow and synthesized by meta-analysis. RESULTS: We found that gut bacterial diversity of COVID-19 patients in both the acute and recovery phases was consistently lower than non-COVID-19 individuals. Microbial differential abundance analysis showed depletion of anti-inflammatory butyrate-producing bacteria and enrichment of taxa with pro-inflammatory properties in COVID-19 patients during the acute phase compared to non-COVID-19 individuals. Analysis of microbial communities showed that the gut microbiota of COVID-19 recovered patients were still in unhealthy ecostates. CONCLUSIONS: Our results provided a comprehensive synthesis to better understand gut microbial perturbations associated with COVID-19 and identified underlying biomarkers for microbiome-based diagnostics and therapeutics.

Absence of TNF Leads to Alternative Activation in Peritoneal Macrophages in Experimental Listeria Monocytogenes Infection.

Macrophages are crucial effectors of innate immunity against the pathogenic bacterium Listeria monocytogenes. The pro-inflammatory cytokine tumour necrosis factor-α (TNF) has been shown to be crucial for resistance to L. monocytogenes and mice deficient in TNF signalling succumb quickly after infection. However, the mechanisms underlying TNF-mediated defence against L. monocytogenes infection have not been completely elucidated. Here, we demonstrate that TNF concurrently functions to support a pro-inflammatory M1 phenotype while actively blocking macrophage polarization to the M2 phenotype. Compared to WT mice, peritoneal macrophages in TNF-deficient mice inoculated with L. monocytogenes respond with M2 polarization by upregulating Arg1. Consistently, TNF blockade in vitro resulted in M2 polarization in peritoneal macrophages during L. monocytogenes infection. Additionally, TNF promotes the transition from M2 to M1 polarization in peritoneal macrophages. Further investigation of peritoneal macrophage polarization suggested the NF-κB pathway is involved in the TNF-dependent M2 to M1 shift. Conversely, treatment of peritoneal macrophage with a PPARγ agonist blunted the expression of M1 genes induced by TNF and reduced NF-κB signalling pathway activation. Competing signalling mechanisms therefore play an essential role in the ability of peritoneal macrophage to resolve L. monocytogenes infections with TNF playing an essential role in driving M1 polarization.Abbreviations: LPM: large peritoneal macrophage; SPM: small peritoneal macrophage; LLO: listeriolysin O; iNOS: inducible nitric oxide synthase; DCs: dendritic cells.

The characteristics of and responses to the two COVID-19 outbreak waves in Hebei Province of China, January 2020 to February 2021.

Hebei Province was affected by two coronavirus disease 2019 (COVID-19) outbreak waves during the period 22 January 2020 through 27 February 2020 (wave 1) and 2 January 2021 through 14 February 2021 (wave 2). To evaluate and compare the epidemiological characteristics, containment delay, cluster events and social activity, as well as non-pharmaceutical interventions of the two COVID-19 outbreak waves, we examined real-time update information on all COVID-19-confirmed cases from a publicly available database. Wave 1 was closely linked with the COVID-19 pandemic in Wuhan, whereas wave 2 was triggered, to a certain extent, by the increasing social activities such as weddings, multi-household gatherings and church events during the slack agricultural period. In wave 2, the epidemic spread undetected in the rural areas, and people living in the rural areas had a higher incidence rate than those living in the urban areas (5.3 vs. 22.0 per 1 000 000). Furthermore, Rt was greater than 1 in the early stage of the two outbreak waves, and decreased substantially after massive non-pharmaceutical interventions were implemented. In China's 'new-normal' situation, development of targeted and effective intervention remains key for COVID-19 control in consideration of the potential threat of new coronavirus strains.

Separation method of bending and torsion in shape sensing based on FBG sensors array.

This paper presents a theoretical method for separating bending and torsion of shape sensing sensor to improve sensing accuracy during its deformation. We design a kind of shape sensing sensor by encapsulating three fibers on the surface of a flexible rod and forming a triangular FBG sensors array. According to the configuration of FBG sensors array, we derive the relationship between bending curvature and bending strain, and set up a function about the packaging angle of FBG sensor and strain induced by torsion under different twist angles. Combined with the influence of bending and torsion on strain, we establish a nonlinear matrix equation resolving three unknown parameters including maximum strain, bending direction and wavelength shift induced by torsion and temperature. The three parameters are sufficient to separate bending and torsion, and acquire two scalar functions including curvature and torsion, which could describe 3D shape of rod according to Frenet-Serret formulas. Experimental results show that the relative average error of measurement about maximum strain, bending direction is respectively 2.65% and 0.86% when shape-sensing sensor is bent into an arc with a radius of 260 mm. The separating method also applied to 2D shape and 3D shape of reconstruction, and the absolute spatial position maximum error is respectively 3.79mm and 11.10mm when shape-sensing sensor with length 500mm is bent into arc shape with a radius 260mm and helical curve. The experiment results verify the feasibility of separating method, which would provide effective parameters for precise 3D reconstruction model of shape sensing sensor.

Stabilizing amorphous Sb by adding alien seeds for durable memory materials.

The thermal stability of the amorphous phase is a key property of phase-change memory, which limits the data retention time and device reliability. The high thermal stability of memory devices enables their applications in harsh environments and under extreme conditions. Here, we discovered that the alloying of C, Si and Ge significantly improves the stability of amorphous Sb by adding "alien" tetrahedral seeds to the octahedral matrix. This doping strategy impedes the crystallization at elevated temperatures so that the crystallization temperature of Sb is increased by 170-220 °C. The mechanism is systematically investigated by ab initio molecular dynamics simulations and classical crystal growth theory. We confirm that the alien tetrahedral bonds increase the activation energy of atomic migration upon crystallization. Our results demonstrate an effective alloying strategy to improve the thermal stability of phase change memory, paving the way for the design of durable memory devices.

Assessment of the circulating cell-free DNA marker association with diagnosis and prognostic prediction in patients with lymphoma: a single-center experience.

Circulating cell-free DNA (ccfDNA) has been shown to be associated with the clinical characteristics and prognosis of cancer patients. Our objective was to assess whether the concentration and integrity index of ccfDNA in plasma may be useful for diagnosing and monitoring the progression of patients with lymphoma. We included plasma samples from 174 lymphoma patients and 80 healthy individuals. The total concentration of ccfDNA was determined using a fluorometry method, and the DNA integrity index (DII), which is the ratio of longer to shorter DNA fragments, for the APP gene was detected using real-time quantitative PCR. The median levels of the ccfDNA concentration and the DII in patients with lymphoma were significantly higher than those in controls (both P < 0.0001). Increases in the ccfDNA concentration and the DII were associated with advanced stage disease, elevated lactate dehydrogenase levels, and a higher prognosis score. In patients with diffuse large B cell lymphoma (DLBCL), high levels of ccfDNA (both concentration and the DII) showed an inferior 2-year progression-free survival (PFS) (P = 0.001; P < 0.0001, respectively). Our study provides quantitative and qualitative evidence in favor of using ccfDNA analysis in lymphoma patients for diagnostic and prognostic assessments.

A Novel Sandwich-Structured Phase Change Composite with Efficient Photothermal Conversion and Electromagnetic Interference Shielding Interface.

Stability and multifunctionality greatly extend the applications of phase change materials (PCMs) for thermal storage and management. Herein, CuS and Fe3O4 nanoparticles were successfully loaded onto cotton-derived carbon to develop a multifunctional interface with efficient photothermal conversion and electromagnetic interference (EMI) shielding properties. 1,3:2,4-di-(3,4-dimethyl) benzylidene sorbitol (DMDBS) and expanded graphite (EG) formed an organic/inorganic three-dimensional network framework to encapsulate 1-octadecanol (OD) by self-assembly. Finally, multifunctional shape-stabilized PCMs (SSPCMs) with the sandwich structure were prepared by the hot-press process. Multifunctional SSPCMs with high load OD (91%) had favorable thermal storage density (200.6 J/g), thermal stability, and a relatively wider available temperature range with improved thermal conductivity to support the thermal storage and management realization. Furthermore, due to the synergistic enhancement of two nanoparticles and the construction of the carbon network with cotton carbon and EG, highly efficient photothermal conversion (94.4%) and EMI shielding (68.9 dB average, X-band) performance were achieved at about 3 mm thickness, which provided the possibility of the multifunctional integration of PCMs. Conclusively, this study provides new insights towards integrating solar energy utilization with the comprehensive protection of related electronics.

Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing.

Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.

Superfast Zincophilic Ion Conductor Enables Rapid Interfacial Desolvation Kinetics for Low-Temperature Zinc Metal Batteries.

Low-temperature rechargeable aqueous zinc metal batteries (AZMBs) as highly promising candidates for energy storage are largely hindered by huge desolvation energy barriers and depressive Zn2+ migration kinetics. In this work, a superfast zincophilic ion conductor of layered zinc silicate nanosheet (LZS) is constructed on a metallic Zn surface, as an artificial layer and ion diffusion accelerator. The experimental and simulation results reveal the zincophilic ability and layer structure of LZS not only promote the desolvation kinetics of [Zn(H2O)6]2+ but also accelerate the Zn2+ transport kinetics across the anode/electrolyte interface, guiding uniform Zn deposition. Benefiting from these features, the LZS-modified Zn anodes showcase long-time stability (over 3300 h) and high Coulombic efficiency with ≈99.8% at 2 mA cm-2, respectively. Even reducing the environment temperature down to 0 °C, ultralong cycling stability up to 3600 h and a distinguished rate performance are realized. Consequently, the assembled Zn@LZS//V2O5-x full cells deliver superior cyclic stability (344.5 mAh g-1 after 200 cycles at 1 A g-1) and rate capability (285.3 mAh g-1 at 10 A g-1) together with a low self-discharge rate, highlighting the bright future of low-temperature AZMBs.

Two-stage multi-task deep learning framework for simultaneous pelvic bone segmentation and landmark detection from CT images.

PURPOSE: Pelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the accuracies of bone segmentation and landmark detection, leading to improper surgery planning and potential operative complications. METHODS: This work proposes a two-stage multi-task algorithm to improve the accuracy of pelvic bone segmentation and landmark detection, especially for the diseased cases. The two-stage framework uses a coarse-to-fine strategy which first conducts global-scale bone segmentation and landmark detection and then focuses on the important local region to further refine the accuracy. For the global stage, a dual-task network is designed to share the common features between the segmentation and detection tasks, so that the two tasks mutually reinforce each other's performance. For the local-scale segmentation, an edge-enhanced dual-task network is designed for simultaneous bone segmentation and edge detection, leading to the more accurate delineation of the acetabulum boundary. RESULTS: This method was evaluated via threefold cross-validation based on 81 CT images (including 31 diseased and 50 healthy cases). The first stage achieved DSC scores of 0.94, 0.97, and 0.97 for the sacrum, left and right hips, respectively, and an average distance error of 3.24 mm for the bone landmarks. The second stage further improved the DSC of the acetabulum by 5.42%, and this accuracy outperforms the state-of-the-arts (SOTA) methods by 0.63%. Our method also accurately segmented the diseased acetabulum boundaries. The entire workflow took ~ 10 s, which was only half of the U-Net run time. CONCLUSION: Using the multi-task networks and the coarse-to-fine strategy, this method achieved more accurate bone segmentation and landmark detection than the SOTA method, especially for diseased hip images. Our work contributes to accurate and rapid design of acetabular cup prostheses.

Towards low-temperature dendrite-free zinc anode by constructing functional MXene buffer layer with duplex zincophilic sites.

Dendrite growth and side reactions of zinc metal anode have severely limited the practical application of aqueous zinc ion batteries (AZIBs). Herein, we introduce an artificial buffer layer composed of functional MXene (Ti3CN) for zinc anodes. The synthesized Ti3CN exhibits superior conductivity and features duplex zincophilic sites (N and F). These characteristics facilitate the homogeneous deposition of Zn2+, accelerate the desolvation process of hydrated Zn2+, and reduce the nucleation overpotential. The Ti3CN-protected Zn anode demonstrates significantly enhanced reversibility compared to bare Zn anode during long-term cycling, achieving a cumulative plating capacity of 10,000 mAh cm-2 at 10 mA cm-2. In Ti3CN-Zn||Cu asymmetric cell, it maintains nearly 100 % Coulombic efficiency over 2500 cycles at 2 mA cm-2. Furthermore, the assembled Ti3CN-Zn//δ-K0.51V2O5 (KVO) full cell exhibit a low capacity decay rate of 0.002 % per cycle at 5 A/g. Even at 0 °C, the Ti3CN-Zn symmetric cell maintains steady cycling for 2000 h. This study introduces a novel approach for designing artificial solid electrolyte interlayers for commercial AZIBs.

Investigating the Phase Transition Kinetics of 1-Octadecanol/Sorbitol Derivative/Expanded Graphite Composite Phase Change Material with Isoconversional and Multivariate Non-Linear Regression Methods.

Organic composite phase change materials (PCMs) have been extensively studied, and it is important to investigate the effect of added components on the phase change process of the organic matrix. Herein, the phase transition process of the composite PCM with 1-octadecanol (OD) as the matrix adsorbed by a network framework composed of 1,3:2,4-di-(3,4-dimethyl) benzylidene sorbitol (DMDBS) and expanded graphite (EG) was measured using differential scanning calorimetry (DSC) at several linear heating rates. Using isoconversional and multivariate non-linear regression methods, a two-step consecutive reaction model for the composite PCM was established, while the apparent activation energies and pre-exponential factors were determined. The reaction mechanism of the first step was altered compared to pure OD, while the activation energies significantly decreased at the initial stage of the phase transition process and increased at the later stage. Combined with microscopic morphology analysis, the main reasons were the size and nanoconfinement effect. The predictions of the composite PCM under various conditions suggested that the composite PCM had a wider available temperature range compared to pure OD. This research provided a new idea for the in-depth study of the phase transition process of organic composite PCMs, which was helpful for the evaluation of organic composite PCMs.

Double Carbon Networks Reinforce the Thermal Storage and Thermal Transfer Properties of 1-Octadecanol Phase Change Materials.

Using thermal storage materials with excellent thermal properties in the energy utilization system enables efficient use of renewable energy sources. Organic phase change materials (PCMs) have the advantages of high heat storage density, no corrosion, and low cost, but low thermal conductivity and insufficient heat transfer capacity have always been the bottlenecks in their application. In this paper, melamine foam@ reduction graphene oxide (MF@rGO) and carbon foam@ reduction graphene oxide (CF@rGO) composite foams with double carbon networks were prepared by self-assembly method and further employed in 1-octadecinal (OD) PCMs. The microstructure, chemical composition, phase change behavior, thermal conductivity, and photothermal conversion performance of MF@rGO/OD and CF@rGO/OD were studied in detail using SEM, FTIR, Raman DSC, and LFA. The melting and solidification enthalpies of CF@rGO/OD composite PCMs were 208.3 J/g and 191.4 J/g, respectively, its thermal conductivity increased to 1.54 W/m·K, which is 6.42 times that of pure OD. The porous structure and high thermal conductivity of the double carbon network substantially enhance the efficiency of energy storage and release in composite PCMs. CF@rGO/OD composite PCMs have excellent heat storage performance and heat transfer capacity, and a wide range of application prospects in the fields of low-temperature solar heat storage, precision instrument temperature control, and intelligent buildings.

Effect of Nano Ni Particles on the Microstructure and Thermophysical Properties of Sn-Bi-Zn Heat Transfer and Heat Storage Alloys.

The specific heat capacity plays a crucial role in influencing the heat transfer efficiency of materials. Considering the relatively low specific heat capacity of metals, this study focuses on investigating the impact of second-phase nano Ni particles on the microstructure and thermophysical properties of the alloy matrix. The alloys' phase compositions and microstructures were examined using X-ray diffraction phase analysis (XRD), electron probe micromorphology analysis (EPMA), and X-ray fluorescence spectroscopy (XRF). Furthermore, the thermophysical properties of the alloys were comprehensively analyzed through the employment of a differential scanning calorimeter (DSC) and the laser flash method (LFA). The addition of second-phase nanoparticles significantly increased the specific heat capacity of the alloy in the liquid state; however, the phenomenon of nanoparticle agglomeration diminishes this improvement. The analysis of the specific heat enhancement mechanism indicates that ordered states are formed between the second-phase solid nanoparticles and the melted metal in the liquid state. With the increase in temperature, the destruction of these ordered states requires additional heat, resulting in the increase of specific heat capacity.

Understanding structure-performance relationships of CoOx/CeO2 catalysts for NO catalytic oxidation: Facet tailoring and bimetallic interface designing.

Crystalline structure and bimetallic interaction of metal oxides are essential factors to determine the catalytic activity. Herein, three different CoOx/CeO2 catalysts, employing CeO2 nanorods (predominately exposed {110 facet), CeO2 nanopolyhedra ({111} facet) and CeO2 nanocubes ({100} facet) as the supports, are successfully prepared for investigating the effect of exposed crystal facets and bimetallic interface interaction on NO oxidation. In comparison with the {111} and {100} facets, the exposed crystal facet {110} exists the best superiority to anchor and stabilize Co species. Moreover, ultra-small CoOx clusters composed of strong Co-O coordination shells with minor Co-O-Ce interaction are formed and uniformly dispersed on the CeO2 nanorods. Structural characterizations reveal that the active exposed crystal facet {110} and the strong bimetallic interface interaction in CoOx/CeO2-nanorods (R-CC) result in more structural defect, endowing it with abundant oxygen vacancies, excellent reducibility and strong adsorption capacity. The DRIFTs spectra further indicate that the exposed crystal facet {110} has a significant promoting effect on the strength of nitrates compared with {111} and {100} facets. The bimetallic interface interaction not only significantly facilitates the formation of nitrate species at lower temperature, but also effectively suppresses the generation of sulfate and lower the sulphation rate. Therefore, R-CC catalyst exhibits the maximum NO oxidation activity with the conversion of 86.4 % at 300 °C and still sustains its high activity under cyclic condition or 50 ppm SO2. The provided crystalline structure and interaction-enhanced strategy sheds light on the design of high-activity NO oxidation catalysts.

Automated tooth crown design with optimized shape and biomechanics properties.

Despite the large demand for dental restoration each year, the design of crown restorations is mainly performed via manual software operation, which is tedious and subjective. Moreover, the current design process lacks biomechanics optimization, leading to localized stress concentration and reduced working life. To tackle these challenges, we develop a fully automated algorithm for crown restoration based on deformable model fitting and biomechanical optimization. From a library of dental oral scans, a conditional shape model (CSM) is constructed to represent the inter-teeth shape correlation. By matching the CSM to the patient's oral scan, the optimal crown shape is estimated to coincide with the surrounding teeth. Next, the crown is seamlessly integrated into the finish line of preparation via a surface warping step. Finally, porous internal supporting structures of the crown are generated to avoid excessive localized stresses. This algorithm is validated on clinical oral scan data and achieved less than 2 mm mean surface distance as compared to the manual designs of experienced human operators. The mechanical simulation was conducted to prove that the internal supporting structures lead to uniform stress distribution all over the model.

Deep learning-based workflow for hip joint morphometric parameter measurement from CT images.

Objective.Precise hip joint morphometry measurement from CT images is crucial for successful preoperative arthroplasty planning and biomechanical simulations. Although deep learning approaches have been applied to clinical bone surgery planning, there is still a lack of relevant research on quantifying hip joint morphometric parameters from CT images.Approach.This paper proposes a deep learning workflow for CT-based hip morphometry measurement. For the first step, a coarse-to-fine deep learning model is designed for accurate reconstruction of the hip geometry (3D bone models and key landmark points). Based on the geometric models, a robust measurement method is developed to calculate a full set of morphometric parameters, including the acetabular anteversion and inclination, the femoral neck shaft angle and the inclination, etc. Our methods were validated on two datasets with different imaging protocol parameters and further compared with the conventional 2D x-ray-based measurement method.Main results. The proposed method yields high bone segmentation accuracies (Dice coefficients of 98.18% and 97.85%, respectively) and low landmark prediction errors (1.55 mm and 1.65 mm) on both datasets. The automated measurements agree well with the radiologists' manual measurements (Pearson correlation coefficients between 0.47 and 0.99 and intraclass correlation coefficients between 0.46 and 0.98). This method provides more accurate measurements than the conventional 2D x-ray-based measurement method, reducing the error of acetabular cup size from over 2 mm to less than 1 mm. Moreover, our morphometry measurement method is robust against the error of the previous bone segmentation step. As we tested different deep learning methods for the prerequisite bone segmentation, our method produced consistent final measurement results, with only a 0.37 mm maximum inter-method difference in the cup size.Significance. This study proposes a deep learning approach with improved robustness and accuracy for pelvis arthroplasty planning.

Silver-catalyzed decarboxylative alkynylation of aliphatic carboxylic acids in aqueous solution.

C(sp(3))-C(sp) bond formations are of immense interest in chemistry and material sciences. We report herein a convenient, radical-mediated and catalytic method for C(sp(3))-C(sp) cross-coupling. Thus, with AgNO(3) as the catalyst and K(2)S(2)O(8) as the oxidant, various aliphatic carboxylic acids underwent decarboxylative alkynylation with commercially available ethynylbenziodoxolones in aqueous solution under mild conditions. This site-specific alkynylation is not only general and efficient but also functional group compatible. In addition, it exhibits remarkable chemo- and stereoselectivity.