First-Principles Study of Discharge Products and Their Stability for Lithium-Nitrogen Batteries.

Li-N2 batteries present a relatively novel approach to N2 immobilization, and an advanced N2/Li3N cycling method is introduced in this study. The low operating overpotential of metal-air batteries is quite favorable to their stable cycling performance, providing a prospect for the development of a new type of battery with extreme voltage. The battery system of Li-N2 uses N2 as the positive electrode, lithium metal as the negative electrode, and a conductive medium containing soluble lithium salts as the electrolyte. In accordance with its voltage-distribution trend, a variety of lithium-nitrogen molecule intermediates are produced during the discharge process. There is a lack of theoretical description of material changes at the microscopic level during the discharge process. In this paper, the first-principles approach is used to simulate and analyze possible material changes during the discharge process of Li-N2 batteries. The discharge process is simulated on a 4N-graphene anode substrate model, and simulations of its electrostatic potential, Density of States (DOS), HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) aspects confirm that the experimentally found Li3N becomes the final stabilized product of the Li-N2 battery. It can also be seen in the density of states that graphene with adsorption of 4N transforms from semiconducting to metallic properties. In addition, the differential charge also indicates that the Li-N2 material has a strong adsorption effect on the substrate, which can play the dual role of electricity storage and nitrogen fixation.

Electronic Properties of CrB/Co2CO2 Superlattices by Multiple Descriptor-Based Machine Learning Combined with First-Principles.

In recent times, newly unveiled 2D materials exhibiting exceptional characteristics, such as MBenes and MXenes, have gained widespread application across diverse domains, encompassing electronic devices, catalysis, energy storage, sensors, and various others. Nonetheless, numerous technical bottlenecks persist in the development of high-performance, structurally flexible, and adjustable electronic device materials. Research investigations have demonstrated that 2D van der Waals superlattices (vdW SLs) structures comprising materials exhibit exceptional electrical, mechanical, and optical properties. In this work, the advantages of both materials are combined and compose the vdW SLs structure of MBenes and MXenes, thus obtaining materials with excellent electronic properties. Furthermore, it integrates machine learning (ML) with first-principles methods to forecast the electrical properties of MBene/MXene superlattice materials. Initially, various configurations of MBene/MXene superlattice materials are explored, revealing that distinct stacking methods exert significant influence on the electronic structure of MBene/MXene materials. Specifically, the BABA-type stacking of CrB (layer A) and Co2CO2 MXene (layer B) is most stable configureation. Subsequently, multiple descriptors of the structure are constructed to predict the density of states  of vdW SLs through the employment of ML techniques. The best model achieves a mean absolute error (MAE) as low as 0.147 eV.

Selecting non-metal doped FeS2 as efficient sulfur cathode host for enhanced Li-S redox chemistry.

Lithium-sulfur batteries (LSBs) are considered as the development direction of the new generation energy storage system due to their high energy density and low cost. The slow redox kinetics of sulfur and the shuttle effect of lithium polysulfide (LiPS) are considered to be the main obstacles to the practical application of LSBs. Transition-metal sulfide as the cathode host can improve the Li-S redox chemistry. However, there has been no investigation of the application of FeS2 host in Li-S redox chemistry. Applying the first-principles calculations, we investigated the formation energy, band gap, Li+ diffusion, adsorption energy, catalytic performance and Li2 S decomposition barrier of FeAx S2-x (A=N, P, O, Se; x=0, 0.125, 0.25, 0.375) to explore the Li-S redox chemistry and finally select excellent host material. FeA0.25 S1.75 (A=P, Se) has a low Li+ diffusion barrier and superior electronic conductivity. FeO0.25 S1.75 is more favorable for LiPS adsorption, followed by FeP0.25 S1.75 . FeP0.25 S1.75 (001) shows a low overpotential for the Li-S redox chemistry. In summary, FeP0.25 S1.75 has more application potential in LSBs due to its physical and chemical properties, followed by FeSe0.25 S1.75 . This work provides theoretical guidance for the design and selection of the sulfur cathode host materials in LSBs.

First-principles study of the discharge electrochemical and catalytic performance of the sulfur cathode host Fe0.875M0.125S2 (M = Ti, V).

Lithium-sulfur batteries (LSBs) are one of the most promising energy storage devices with high energy density. However, their application and commercialization are hampered by the slow Li-S redox chemistry. Fe0.875M0.125S2 (M = Ti, V), as the sulfur cathode host, enhances the Li-S redox chemistry. FeS2 with Pa3̄ is transformed into Li2FeS2 with P3̄m1 after discharge. The structure changes and physicochemical properties during Fe0.875M0.125S2 discharge process are further investigated to screen out the sulfur cathode host materials with the best comprehensive properties. The discharge structure of Fe0.875M0.125S2 is verified by the thermodynamic stability of Li-deficient phases, voltage and capacity based on Monte Carlo methods. Fe0.875M0.125S2 with Pa3̄ is transformed into Li2Fe0.875M0.125S2 with P3̄m1 after discharge. Using the first-principles calculations, the physicochemical properties of Li2Fe0.875M0.125S2 are systematically investigated, including the formation energy, voltage, theoretical capacity, electrical conductivity, Li+ diffusion, catalytic performance and Li2S oxidation decomposition. The average redox voltage of Li2Fe0.875V0.125S2 is higher than that of Li2Fe0.875Ti0.125S2. Li2Fe0.875M0.125S2 shows metallic properties. Li2Fe0.875V0.125S2 is more beneficial to the reduction reaction of Li2S2 and Li2S oxidation decomposition. Fe0.875V0.125S2 has more potential as the sulfur cathode host than Fe0.875Ti0.125S2 in LSBs. A new strategy for the selection of the sulfur cathode host material for LSBs is provided by this work.

Double strengthening induced by grain boundary segregation of solute elements in gradient nano Ni-Co alloys.

Gradient induced unusual strain hardening achieves the equilibrium of the strength and plasticity of alloys, and is an important strategy for the optimization of the mechanical properties of metals and alloys. The segregation of solute elements can greatly improve the grain boundary stability, inhibit grain coarsening and promote the mechanical strength of the alloy. In our efforts, the segregation structure of the solute element Co was designed and added to the gradient nano Ni-Co alloy, and the two strengthening strategies were applied simultaneously in one structure. The mechanical strength of the alloy achieved a second increase based on the unique combination of gradient induced strain hardening and high plasticity, especially the yield strength of alloy increase amplitude reach to 42%. This provides a positive direction for the alloy strengthening strategy. In the process of secondary strengthening, the micro-mechanism is divided into two stages: in the first stage, the gradient strain provides the alloy with geometrically necessary dislocations and a multi-axial stress state, and the existence of large numbers of geometrically necessary dislocations creates good conditions for the second stage strengthening. In the second stage, the solute segregation induced stable grain boundaries produce a strong pinning effect on the geometrically necessary dislocation, which realizes the coupling of grain boundary strengthening and dislocation strengthening. This provides a new strengthening strategy and positive theoretical guidance for the experimental preparation of advanced alloys with excellent properties.

In-Wheel Motor Fault Diagnosis Using Affinity Propagation Minimum-Distance Discriminant Projection and Weibull-Kernel-Function-Based SVDD.

To effectively ensure the operational safety of an electric vehicle with in-wheel motor drive, a novel diagnosis method is proposed to monitor each in-wheel motor fault, the creativity of which lies in two aspects. One aspect is that affinity propagation (AP) is introduced into a minimum-distance discriminant projection (MDP) algorithm to propose a new dimension reduction algorithm, which is defined as APMDP. APMDP not only gathers the intra-class and inter-class information of high-dimensional data but also obtains information on the spatial structure. Another aspect is that multi-class support vector data description (SVDD) is improved using the Weibull kernel function, and its classification judgment rule is modified into a minimum distance from the intra-class cluster center. Finally, in-wheel motors with typical bearing faults are customized to collect vibration signals under four operating conditions, respectively, to verify the effectiveness of the proposed method. The results show that the APMDP's performance is better than traditional dimension reduction methods, and the divisibility is improved by at least 8.35% over the LDA, MDP, and LPP. A multi-class SVDD classifier based on the Weibull kernel function has high classification accuracy and strong robustness, and the classification accuracies of the in-wheel motor faults in each condition are over 95%, which is higher than the polynomial and Gaussian kernel function.

Insights into the electrochemical properties of Li2FeS2 after FeS2 discharging.

All-solid-state lithium-sulfur batteries (ASSLSBs) have high reversible characteristics owing to the high redox potential, high theoretical capacity, high electronic conductivity, and low Li+ diffusion energy barrier in the cathode. Monte Carlo simulations with cluster expansion, based on the first-principles high-throughput calculations, predicted a phase structure change from Li2FeS2 (P3̄M1) to FeS2 (PA3̄) during the charging process. LiFeS2 is the most stable phase structure. The structure of Li2FeS2 after charging was FeS2 (P3̄M1). By applying the first-principles calculations, we explored the electrochemical properties of Li2FeS2 after charging. The redox reaction potential of Li2FeS2 was 1.64 to 2.90 V, implying a high output voltage of ASSLSBs. Flatter voltage step plateaus are important for improving the electrochemical performance of the cathode. The charge voltage plateau was the highest from Li0.25FeS2 to FeS2 and followed from Li0.375FeS2 to Li0.25FeS2. The electrical properties of LixFeS2 remained metallic during the Li2FeS2 charging process. The intrinsic Li Frenkel defect of Li2FeS2 was more conducive to Li+ diffusion than that of the Li2S Schottky defect and had the largest Li+ diffusion coefficient. The good electronic conductivity and Li+ diffusion coefficient of the cathode implied a better charging/discharging rate performance of ASSLSBs. This work theoretically verified the FeS2 structure after Li2FeS2 charging and explored the electrochemical properties of Li2FeS2.

Insight into the Electronic Properties of Semiconductor Heterostructure Based on Machine Learning and First-Principles.

A first-principles approach is a powerful means of gaining insight into the intrinsic structure and properties of materials. However, with the implementation of material genetic engineering, it is still a challenging road to discover materials with high satisfaction. One alternative is to employ machine-learning techniques to mine data and predict performance. In this present contribution, the method is taken to predict the band gap opening value of graphene in a heterostructure. First, the data of 2076 binary compounds in the Materials Project library are used to achieve visual dimensionality reduction of the data set through a t-distributed stochastic neighbor embedding (t-SNE) algorithm in unsupervised learning. Then, a series of semiconductor components are screened out and form heterostructures with graphene. Second, by means of the ensemble learning EXtreme Gradient Boost (XGBoost) algorithm and support vector machine (SVM) technology, two prediction frameworks are built to predict the band gap opening value of the graphene in the system. Finally, density functional theory (DFT) is used to calculate the energy band and density of states for comparison. Analysis shows that the prediction model has an accuracy rate of 88.3%, and there is little difference between prediction results and calculation results. We anticipate that this framework model would have fascinating applications in predicting the electronic properties of various multiphase materials.

Ferroelectric polarization effect on the photocatalytic activity of Bi0.9Ca0.1FeO3/CdS S-scheme nanocomposites.

BiFeO3 (BFO), as a kind of narrow band-gap semiconductor material, has gradually emerged advantages in the application of photocatalysis. In this paper, Ca doped BFO nanoparticles Bi0.9Ca0.1FeO3 (BCFO) were prepared by sol-gel method. And BCFO and CdS nanocomposites with two morphologies were obtained by controlling the time of loading CdS under a low temperature liquid phase process. It is found that the band gap becomes narrower after doping Ca into BFO, which is conducive to the absorption of visible light. Among all the samples, the composite of CdS nanowires and BCFO nanoparticles obtained by reaction time of 10 min has the best photocatalytic performance. The degradation rate of Methyl Orange solution was 94% after 90 min under visible light irradiation, which was much higher than that of pure BCFO and CdS. Furthermore, significant enhancement in the degradation rate (100% degradation in 60 min) can be achieved in poled samples after electric polarization process. The highest degradation rate is due to the promoted separation of photogenerated carriers induced by the internal polarization field and the formation of S-scheme heterostructure between BCFO and CdS. Such BCFO-CdS nanocomposites may bring new insights into designing highly efficient photocatalyst.

Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor.

To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).

Intelligent diagnosis of mechanical faults of in-wheel motor based on improved artificial hydrocarbon networks.

For the driving safety of electric vehicle (EV), intelligent diagnosis based on artificial hydrocarbon networks (AHNs) is proposed to detect mechanical faults of in-wheel motor (IWM) which is a promising force pattern of EV. AHNs, a novel mathematical model of supervised learning algorithm, can encapsulate or inherit or mix any information, then are adapted to deal with serious external interference and the variable operating conditions. Based on the basic AHNs, complex error function is proposed to optimize more information of classification targets, and distance error ratio is defined to evaluate the performance. Then, the improved AHNs is employed to build two intelligent diagnosis systems namely one-stop diagnosis and sequential diagnosis, which select the same and different symptom parameters as the object of a follow-on process, respectively. The effectiveness of the proposed methods is validated by two case studies of Case Western Reserve University dataset and mechanical faults data from IWM's test bench.

Cluster expansion Monte Carlo study of indium-aluminum segregation and homogenization in CuInSe2-CuAlSe2 pseudobinary alloys.

The influence of temperature and Al content on the segregation and homogenization behaviour of In-Al atoms in CuIn1-xAlxSe2 (CIAS) pseudobinary alloys is studied using a combination of cluster expansion Monte Carlo simulations and first-principles calculations. Such alloys are promising materials for a number of solar-energy-related applications. We found that the segregation of In-Al atoms in CIAS alloys with different Al contents occurs at relatively low temperatures. The cluster morphology of Al(In) atoms in CIAS alloys at 73 K appears in an ellipsoidal, rod-like or lamellar form, depending on the Al(In) content. The spatial distribution of In-Al atoms becomes homogeneous as the temperature increases. By determining the inhomogeneity degree σ of In-Al distributions in CIAS alloys at a series of temperatures, we found that the variation of σ with temperature (T) for all the considered CIAS alloys are sigmoidal in general and the sharp decrease in σ within a certain temperature range implies the occurrence of inhomogeneous-to-homogeneous phase transition. The inhomogeneity degree σ of CIAS alloys before or after the phase transition (phase segregation) increases as the content of Al(x) and In(1 - x) atoms gets closer. The σ(T) data points obtained by us can be well fitted with the Boltzmann function, which can give several physically meaningful parameters such as the phase transition temperature T0, temperature range of phase transition ΔT and so on. The fitted T0 and ΔT values for CIAS alloys with different Al content were proved to be reliable. The novel method for predicting the T0 and ΔT may be applied to many other binary or pseudobinary material systems with positive formation energy.

Manganese doping mechanism in a CsPbI2Br photovoltaic material: a first-principles study.

As a light absorbing material of perovskite solar cells, Mn-doped CsPbI2Br has a better phase stability than the undoped one. In order to deeply understand the doping mechanism of Mn, the effect of substitutional and interstitial Mn doping on the structural, electronic and optical properties of CsPbI2Br has been investigated by first-principles calculations based on density functional theory. It is found that the binding energy of both the substitutional and the interstitial Mn-doped CsPbI2Br is negative and the binding energy difference between them is only 2.8 meV, which indicates that both the substitutional and the interstitial doping structures should be stable for Mn-doped CsPbI2Br and the latter is slightly preferred over the former due to the lower binding energy. The lattice parameters of CsPbI2Br change oppositely for two Mn-doping cases. Based on the comparative analysis of the electronic structures for CsPbI2Br and Mn-doped CsPbI2Br, we found that the substitutional doping of Mn introduces intermediate bands near the Fermi level, making CsPbI2Br an intermediate band semiconductor; for the interstitial Mn-doped CsPbI2Br the Fermi level enters conduction bands, making it an n-type semiconductor material with enhanced conductivity. The complex dielectric function and the absorption spectrum of Mn-doped and undoped CsPbI2Br were calculated and are basically consistent with the experimental results.

Tensile mechanical performance of Ni-Co alloy nanowires by molecular dynamics simulation.

In this present contribution, tensile mechanical properties of Ni-Co alloy nanowires with Co content from 0 to 20% were studied by molecular dynamics. The simulation results show the alloy nanowire with the Co content of 5% has the highest yield value of 9.72 GPa. In addition, more Frank dislocations were generated during the loading process to improve the performance of the alloy nanowire. The Young's modulus increases little by little from 105.68 to 179.78 GPa with the increase of Co content. Secondly, with the increase of temperature, the yield strength gradually decreases to 2.13 GPa. Young's modulus tends to decrease linearly from 170.7 GPa to 48.21 GPa. At the temperatures of 500 K and 700 K, it is easier to form Frank dislocation and Hirth dislocation, respectively, in the loading process. The peak value of the radial distribution function decreases and the number of peaks decreases, indicating the disappearance of the ordered structure. Finally, after the introduction of the surface and inner void, the yield strength of the nanowire drops about to 8.97 and 6.6 GPa, respectively, and the yield strains drop to 0.056 and 0.043. In the case of the existence of internal void, perfect dislocation and Hirth dislocation can be observed in the structure.

Mesoporous Nanosheet Networked Hybrids of Cobalt Oxide and Cobalt Phosphate for Efficient Electrochemical and Photoelectrochemical Oxygen Evolution.

A novel mesoporous nanosheet networked hybrid comprising Co3 O4 and Co3 (PO4 )2 is successfully synthesized using a facile and scalable method through calcinating the carbon, cobalt hydroxy carbonate, and cobalt phosphate composite precursor. Electron transfer from Co3 O4 to Co3 (PO4 )2 , together with the special networked structure and the porous nature of the nanosheets enable the Co3 (PO4 )2 -Co3 O4 hybrid to have a high oxygen evolution reaction (OER) activity and outstanding stability in alkaline electrolyte, e.g., an overpotential of 270 mV at current density of 10 mA cm-2 , and a Tafel slope of 39 mV dec-1 , which are superior to most non-noble metal-based OER electrocatalysts reported thus far and as well the commercial RuO2 electrocatalyst. Furthermore, Co3 (PO4 )2 -Co3 O4 hybrid is demonstrated to be used as an efficient cocatalyst to enhance the photoelectrochemical OER performance of BiVO4 photoanode. A significantly increased photocurrent density of 3.0 mA cm-2 at 1.23 V (vs reversible hydrogen electrode, RHE), and a potential reduction of 530 mV with respect to that of bare BiVO4 at the photocurrent density of 0.5 mA cm-2 are achieved. The electron transfer-induced enhancement of OER by a hybrid structure may pave the new routes for the design and synthesis of low-cost catalysts for electrochemical and photoelectrochemical oxygen evolution.

Linear-scaling density functional simulations of the effect of crystallographic structure on the electronic and optical properties of fullerene solvates.

In this work, the crystal properties, HOMO and LUMO energies, band gaps, density of states, as well as the optical absorption spectra of fullerene C60 and its derivative phenyl-C61-butyric-acid-methyl-ester (PCBM) co-crystallised with various solvents such as benzene, biphenyl, cyclohexane, and chlorobenzene were investigated computationally using linear-scaling density functional theory with plane waves as implemented in the ONETEP program. Such solvates are useful materials as electron acceptors for organic photovoltaic (OPV) devices. We found that the fullerene parts contained in the solvates are unstable without solvents, and the interactions between fullerene and solvent molecules in C60 and PCBM solvates make a significant contribution to the cohesive energies of solvates, indicating that solvent molecules are essential to keep C60 and PCBM solvates stable. Both the band gap (Eg) and the HOMO and LUMO states of C60 and PCBM solvates are mainly determined by the fullerene parts contained in solvates. Chlorobenzene- and ortho-dichlorobenzene-solvated PCBM are the most promising electron-accepting materials among these solvates for increasing the driving force for charge separation in OPVs due to their relatively high LUMO energies. The UV-Vis absorption spectra of solvent-free C60 and PCBM crystals in the present work are similar to those of C60 and PCBM thin films shown in the literature. Changes in the absorption spectra of C60 solvates relative to the solvent-free C60 crystal are more significant than those of PCBM solvates due to the weaker effect of solvents on the π-stacking interactions between fullerene molecules in the latter solvates. The main absorptions for all C60 and PCBM crystals are located in the ultraviolet (UV) region.

Green Synthesis of Bifunctional Fluorescent Carbon Dots from Garlic for Cellular Imaging and Free Radical Scavenging.

Nitrogen and sulfur codoped carbon dots (CDs) were prepared from garlic by a hydrothermal method. The as-prepared CDs possess good water dispersibility, strong blue fluorescence emission with a fluorescent quantum yield of 17.5%, and excellent photo and pH stabilities. It is also demonstrated that the fluorescence of CDs are resistant to the interference of metal ions, biomolecules, and high ionic strength environments. Combining with low cytotoxicity properties, CDs could be used as an excellent fluorescent probe for cellular multicolor imaging. Moreover, the CDs were also demonstrated to exhibit favorable radical scavenging activity.

Enhanced tolerance to stretch-induced performance degradation of stretchable MnO2-based supercapacitors.

The performance of many stretchable electronics, such as energy storage devices and strain sensors, is highly limited by the structural breakdown arising from the stretch imposed. In this article, we focus on a detailed study on materials matching between functional materials and their conductive substrate, as well as enhancement of the tolerance to stretch-induced performance degradation of stretchable supercapacitors, which are essential for the design of a stretchable device. It is revealed that, being widely utilized as the electrode material of the stretchable supercapacitor, metal oxides such as MnO2 nanosheets have serious strain-induced performance degradation due to their rigid structure. In comparison, with conducting polymers like a polypyrrole (PPy) film as the electrochemically active material, the performance of stretchable supercapacitors can be well preserved under strain. Therefore, a smart design is to combine PPy with MnO2 nanosheets to achieve enhanced tolerance to strain-induced performance degradation of MnO2-based supercapacitors, which is realized by fabricating an electrode of PPy-penetrated MnO2 nanosheets. The composite electrodes exhibit a remarkable enhanced tolerance to strain-induced performance degradation with well-preserved performance over 93% under strain. The detailed morphology and electrochemical impedance variations are investigated for the mechanism analyses. Our work presents a systematic investigation on the selection and matching of electrode materials for stretchable supercapacitors to achieve high performance and great tolerance to strain, which may guide the selection of functional materials and their substrate materials for the next-generation of stretchable electronics.

High interfacial storage capability of porous NiMn2O4/C hierarchical tremella-like nanostructures as the lithium ion battery anode.

Porous hierarchical NiMn2O4/C tremella-like nanostructures are obtained through a simple solvothermal and calcination method. As the anode of lithium ion batteries (LIBs), porous NiMn2O4/C nanostructures exhibit a superior specific capacity and an excellent long-term cycling performance even at a high current density. The discharge capacity can stabilize at 2130 mA h g(-1) within 350 cycles at a current density of 1000 mA g(-1). After a long-term cycling of 1500 cycles, the capacity is still as high as 1773 mA h g(-1) at a high current density of 4000 mA g(-1), which is almost five times higher than the theoretical capacity of graphite. The porous NiMn2O4/C hierarchical nanostructure provides sufficient contact with the electrolyte and fast three-dimensional Li(+) diffusion channels, and dramatically improves the capacity of NiMn2O4/C via interfacial storage.

Identification of multifunctional graphene-gold nanocomposite for environment-friendly enriching, separating, and detecting Hg²âº simultaneously.

By virtue of the specific amalgam of mercury with gold and high specific area of a graphene scaffold, an environment-friendly multifunctional graphene-gold nanocomposite (G-AuNPs) has been identified and prepared by a simple one-pot redox reaction. The resultant G-AuNPs can reversibly enrich about 94% of Hg(2+) in water samples, which can be further separated by only a simple filtration. Importantly, the color of the G-AuNPs suspension exclusively changes from purple-red to light brown upon the addition of Hg(2+) in the presence of ascorbic acid, which can be applied for colorimetric detection of Hg(2+) with a detection limit (3σ, n = 20) of 1.6 × 10(-8) mol·L(-1). Furthermore, using ascorbic acid as reducing agents, both the preparation process and the resultant nanocomposite are nontoxic. To the best of our knowledge, this is the first report to enrich, separate and detect Hg(2+) contaminant simultaneously without causing any secondary pollution.