Search

Passive radiative cooling presents a promising approach to mitigate global warming and reduce energy consumption. Existing coolers, however, face challenges in practical prolonged adoption due to their unappealing design, susceptibility to environmental pollutants, as well as degradation of polymers by ultraviolet (UV) irradiation from sunlight. This study introduces a multifunctional daytime radiative cooler (MDRC) with a dual-layered structure, combining practical functionality with aesthetic value. Its base layer consists of an Ag–TiO 2 –Ag thin film stack, imparting vibrant cyan, magenta, and yellow colors, even maintaining visually appealing from various incident angles, up to 75°, to ensure consistent color display. The top layer, comprising S–SiO 2 nanospheres with UV resistance rather than polymers, not only forms a superhydrophobic surface with a contact angle of 155.502°, enhancing the self-cleaning capability of the MDRC against contamination and dust, but also renders the MDRC with extraordinary anti-aging capacity. Outdoor experiments has shown that the MDRC maintains operation at temperatures above ambient under direct solar irradiation, achieving a substantial temperature reduction of 6.82 °C compared to the same colored commercial coating. After three months of outdoor exposure, the MDRC demonstrated minimal degradation in performance, with decreases of only 0.29 °C during the day and 0.12 °C at night, underscoring its superior UV-resistant aging properties. This MDRC represents a robust solution for diverse durable outdoor raidative cooling applications, advancing toward real-world utilization. • The cooler is designed to be aesthetic and practical. • Demonstrates exceptional superhydrophobicity with a contact angle of 155.502°, ensuring self-cleaning capability. • Achieves a significant temperature reduction of 3.85 °C in outdoor environments compared to colored commercial coatings. • Maintains consistent cooling performance even after extended outdoor exposure, verifying superior UV resistance. [ABSTRACT FROM AUTHOR]

This paper explores the homogeneity of coefficient functions in nonlinear models with functional coefficients and identifies the underlying semiparametric modelling structure. With initial kernel estimates, we combine the classic hierarchical clustering method with a generalised version of the information criterion to estimate the number of clusters, each of which has a common functional coefficient, and determine the membership of each cluster. To identify a possible semi-varying coefficient modelling framework, we further introduce a penalised local least squares method to determine zero coefficients, non-zero constant coefficients and functional coefficients which vary with an index variable. Through the nonparametric kernel-based cluster analysis and the penalised approach, we can substantially reduce the number of unknown parametric and nonparametric components in the models, thereby achieving the aim of dimension reduction. Under some regularity conditions, we establish the asymptotic properties for the proposed methods including the consistency of the homogeneity pursuit. Numerical studies, including Monte-Carlo experiments and two empirical applications, are given to demonstrate the finite-sample performance of our methods. [ABSTRACT FROM AUTHOR]

This article studies stationary functional time series with long‐range dependence, and estimates the memory parameter involved. Semiparametric local Whittle estimation is used, where periodogram is constructed from the approximate first score, which is an inner product of the functional observation and estimated leading eigenfunction. The latter is obtained via classical functional principal component analysis. Under the restrictive condition of constancy of the memory parameter over the function support, and other conditions which include rather unprimitive ones on the first score, the estimate is shown to be consistent and asymptotically normal with asymptotic variance free of any unknown parameter, facilitating inference, as in the scalar time series case. Although the primary interest lies in long‐range dependence, our methods and theory are relevant to short‐range dependent or negative dependent functional time series. A Monte Carlo study of finite sample performance and an empirical example are included. [ABSTRACT FROM AUTHOR]

The Al3FeNd2 single-phase alloy was prepared by melting stoichiometric Al, Fe, and Nd in a nonconsumable arc furnace filled with argon and then homogenizing annealing at a high temperature. The crystal structure of Al3FeNd2 was analyzed by an X-ray powder diffraction technique, Rietveld refinement analysis, and the first-principles simulation calculation method. The results show that the alloy crystallizes in the cubic cell, space group of Fd-3m (no. 227), and the lattice parameters of a = 7.8892 (1) Å, Z = 4, and Dcalc = 5.75 g/cm3. Al3FeNd2 exhibits ferrimagnetic properties at low temperatures and has a Curie temperature (Tc) of 353 K, and its saturation magnetic moment (Ms) is 3.33 μB/f.u. at 2 K. The simulation results also show that Al3FeNd2 is a ferrimagnet with a spontaneous magnetic moment of 4.11 μB/f.u., and it is believed that the spontaneous magnetization of this alloy originates from the electron spin in the Fe(16c)-3d band and Nd(8b)-4f. [ABSTRACT FROM AUTHOR]

A new ternary compound, Al2Fe3Nd, was prepared via arc melting using a non-consumable tungsten electrode under an argon atmosphere with subsequent annealing at 923 K for 30 days. Its crystal structure was determined using the Rietveld refinement of powder X-ray diffraction data. The results show that Al2Fe3Nd has a hexagonal structure in a space group of P6/mmm (No. 191). It is isostructural with the Al2.5Ni2.5Y compound. It has lattice parameters of a = 8.8625(3) Ǻ and c = 4.1475(2) Ǻ with a calculated density Dcalc = 6.460 g/cm3. The residual factors converge to Rp = 0.0946 and Rwp = 0.1214. In the temperature range of 2–500 K, magnetic susceptibility has been measured. The magnetic susceptibility of Al2Fe3Nd follows the Curie–Weiss law in the temperature range of 410–500 K. The effective magnetic moment μeff and paramagnetic Curie temperature θP were estimated to be 8.59 μB and 399.8 K, respectively. The saturation magnetic moment and magnetic hysteresis loop for the compound were measured under room- and low-temperature conditions. The measurements indicate that the saturation magnetization moment declines with increasing temperature, and the compound is ferrimagnetic in the analytical temperature range of 2–300 K. [ABSTRACT FROM AUTHOR]

Ligand exchange was triggered at toluene/water emulsion interface between homogeneous gold nanoparticles (Au NPs) with triphenylphosphine (PPh3) and D-penicillamine (D-PA), resulted in amphiphilic Janus Au NPs with both phosphine and thiolate ligands. TEM and XRD analyses indicate that the product is composed of Au nanocrystals in an average diameter of about 3.7 nm. XPS, FTIR, Raman, and 1H NMR analyses demonstrate that the Au NPs are protected with hydrophilic D-PA molecules and lipophilic PPh3 ligands in a molecular ratio of ca. 1.8. The NOESY analysis and contact angle measurement further suggest that the D-PA and PPh3 molecules are spontaneously separated to form compartmentalized hydrophilic and lipophilic regions on the individual Au NPs, which exhibit good catalytic performance and recyclability on the reduction of 4-nitrophenol. The results demonstrate that amphiphilic Janus Au NPs can be synthesized by partly exchange of PPh3 with D-PA at toluene/water emulsion interface and are potentially applicable for other phosphine/thiolate pairs to modify Au NPs. [ABSTRACT FROM AUTHOR]

We introduce methods and theory for functional or curve time series with long-range dependence. The temporal sum of the curve process is shown to be asymptotically normally distributed, the conditions for this covering a functional version of fractionally integrated autoregressive moving averages. We also construct an estimate of the long-run covariance function, which we use, via functional principal component analysis, in estimating the orthonormal functions spanning the dominant subspace of the curves. In a semiparametric context, we propose an estimate of the memory parameter and establish its consistency. A Monte Carlo study of finite-sample performance is included, along with two empirical applications. The first of these finds a degree of stability and persistence in intraday stock returns. The second finds similarity in the extent of long memory in incremental age-specific fertility rates across some developed nations. for this article are available online. [ABSTRACT FROM AUTHOR]

This article introduces a new class of functional‐coefficient predictive regression models, where the regressors consist of auto‐regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one‐step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor‐augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite‐sample performance of the proposed model and methodology. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A facile one-pot strategy available for the batch synthesis of amphiphilic Janus Au NPs. • Toluene/water emulsion drops offer a large interface to accelerate the reaction. • More hydrophilic and lipophilic ligands can be used to prepare amphiphilic Janus Au NPs. Abstract Amphiphilic Janus Au NPs were synthesized by emulsion reaction with presence of d -penicillamine (D-PA) and benzyl mercaptan (BM) molecules at water and toluene phases, respectively. TEM and XRD characterizations indicate that the reaction product is composed of Au nanocrystals in size of 2.9 nm. XPS, FTIR, Raman and 1H NMR analyses suggest that the capping ligands on the Au NPs are D-PA and BM, where the ratio of D-PA to BM is ca. 1.7. Contact angle measurement and NOESY analysis further reveal that the hydrophilic D-PA and lipophilic BM are separated into two distinct hemispheres on the individual Au NPs. The simple and efficient approach allows large scale preparation of amphiphilic Janus Au NPs at low cost for versatile applications, such as drug delivery, vapor sensors and heterogeneous catalysts. [ABSTRACT FROM AUTHOR]

[Display omitted] • Competitive absorption between triphenylphosphine and thiols is found on gold surface. • Bilateral conversion between hydrophilic and Janus gold nanoparticles is enabled. • Facile separation and reuse of gold catalysts is realized in 4-nitrophenol reduction. • Gold nanocatalysts could be recycled for more than 10 times by Janus modification. Separation and regeneration of ultrafine catalysts in reaction system still remain challenging. In this study, we demonstrate a simple route to recycle gold nanoparticle (Au NP) catalysts by alternating surface wettability. Hydrophilic Au NPs protected with 3-mercapto-1,2-propanediol (MPD) were applied to catalyze the reduction of 4-nitrophenol (4-NP) using sodium borohydride as reductant, and were then separated from the suspension by interfacial ligand exchange with a toluene solution containing triphenylphosphine (TPP), led to amphiphilic Janus Au NPs with both MPD and TPP ligands at the toluene-water interface. The catalyst was regenerated by a reversible ligand exchange with a MPD aqueous solution. Experiments show that interfacial ligand exchange between phosphines and thiols on gold surface is reversible, although the forward and backward rates are different. As a result, the Au NP catalyst capped with MPD exhibited an excellent catalytic activity with an apparent rate constant (k) of 2.79 min−1, and the performance could be maintained for more than 10 regeneration cycles. The facile conversion between hydrophilic and amphiphilic Janus Au NPs provides a new pathway to explore monophasic and multiphasic reactions with convenient catalyst separation and regeneration. [ABSTRACT FROM AUTHOR]

We propose two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags. Our objective is to obtain more accurate estimates and forecasts of time series by using a large number of conditioning variables in a nonparametric way. In the first scheme, we introduce a kernel sure independence screening (KSIS) technique to screen out the regressors whose marginal regression (or autoregression) functions do not make a significant contribution to estimating the joint multivariate regression function; we then propose a semiparametric penalized method of model averaging marginal regression (MAMAR) for the regressors and auto-regressors that survive the screening procedure, to further select the regressors that have significant effects on estimating the multivariate regression function and predicting the future values of the response variable. In the second scheme, we impose an approximate factor modeling structure on the ultra-high dimensional exogenous regressors and use the principal component analysis to estimate the latent common factors; we then apply the penalized MAMAR method to select the estimated common factors and the lags of the response variable that are significant. In each of the two schemes, we construct the optimal combination of the significant marginal regression and autoregression functions. Asymptotic properties for these two schemes are derived under some regularity conditions. Numerical studies including both simulation and an empirical application to forecasting inflation are given to illustrate the proposed methodology. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

We study a semivarying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. A semiparametric estimation methodology with the first-stage local polynomial smoothing is applied to estimate both the constant coefficients in the linear component and the functional coefficients in the nonlinear component. The asymptotic distribution theory for the proposed semiparametric estimators is established under some mild conditions, from which both the parametric and nonparametric estimators are shown to enjoy the well-known super-consistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the developed methodology and results. [ABSTRACT FROM AUTHOR]

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. Simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract: We consider asymptotic expansion of the nonparametric M-estimator in a fixed-design nonlinear regression model when the errors are generated by long-memory linear processes. Under mild conditions, we show that the nonparametric M-estimator is first-order equivalent to the Nadaraya–Watson (NW) estimator, which implies that the nonparametric M-estimator has the same asymptotic distribution as that of the NW estimator. Furthermore, we study the second-order asymptotic expansion of the nonparametric M-estimator and show that the difference between the nonparametric M-estimator and the NW estimator has a limiting distribution after suitable standardization. The nature of the limiting distribution depends on the range of long-memory parameter . We also compare the finite sample behavior of the two estimators through a numerical example when the errors are long-memory. [Copyright &y& Elsevier]

Abstract: A partially time-varying coefficient time series model is introduced to characterize the nonlinearity and trending phenomenon. To estimate the regression parameter and the nonlinear coefficient function, the profile least squares approach is applied with the help of local linear approximation. The asymptotic distributions of the proposed estimators are established under mild conditions. Meanwhile, the generalized likelihood ratio test is studied and the test statistics are demonstrated to follow asymptotic under the null hypothesis. Furthermore, some extensions of the proposed model are discussed and several numerical examples are provided to illustrate the finite sample behavior of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers the nonparametric -estimator in a nonlinear cointegration type model. The local time density argument, which was developed by Phillips and Park (1998)  and Wang and Phillips (2009) , is applied to establish the asymptotic theory for the nonparametric -estimator. The weak consistency and the asymptotic distribution of the proposed estimator are established under mild conditions. Meanwhile, the asymptotic distribution of the local least squares estimator and the local least absolute distance estimator can be obtained as applications of our main results. Furthermore, an iterated procedure for obtaining the nonparametric -estimator and a cross-validation bandwidth selection method are discussed, and some numerical examples are provided to show that the proposed methods perform well in the finite sample case. [Copyright &y& Elsevier]

A robust version of local linear regression smoothers augmented with variable bandwidths is investigated for dependent spatial processes. The (uniform) weak consistency as well as asymptotic normality for the local linear M-estimator (LLME) of the spatial regression function g( x) are established under some mild conditions. Furthermore, an additive model is considered to avoid the curse of dimensionality for spatial processes and an estimation procedure based on combining the marginal integration technique with LLME is applied in this paper. Meanwhile, we present a simulated study to illustrate the proposed estimation method. Our simulation results show that the estimation method works well numerically. [ABSTRACT FROM AUTHOR]

Abstract: We study a random design regression model generated by dependent observations, when the regression function itself (or its -th derivative) may have a change or discontinuity point. A method based on the local polynomial fits with one-sided kernels to estimate the location and the jump size of the change point is applied in this paper. When the jump location is known, a central limit theorem for the estimator of the jump size is established; when the jump location is unknown, we first obtain a functional limit theorem for a local dilated-rescaled version estimator of the jump size and then give the asymptotic distributions for the estimators of the location and the jump size of the change point. The asymptotic results obtained in this paper can be viewed as extensions of corresponding results for independent observations. Furthermore, a simulated example is given to show that our theory and method perform well in practice. [Copyright &y& Elsevier]

Abstract  The asymptotic behavior of S-estimators in a random design linear model with long-range-dependent Gaussian errors is considered. It turns out that the S-estimators of regression parameter and error variance are strongly consistent under mild conditions. Furthermore, the asymptotic distribution of the S-estimator of regression parameter is normal if the design vectors are i.i.d. and is non-normal if the design vectors are long-range dependent Gaussian vectors. We also show that the asymptotic distribution of S-estimator of the error variance is non-normal since the errors are long-range dependent. [ABSTRACT FROM AUTHOR]

Abstract: Let be a set of observations from a stationary jointly associated process and be the conditional median, that is, . We consider the problem of estimating based on the -norm kernel and establish asymptotic normality of the resulting estimator . [Copyright &y& Elsevier]

Let {Xt, t≥1} be a moving average process defined by Xt = (Multiple line equation(s) cannot be represented in ASCII text) bjξt-j , where {bj,j≥0} is a sequence of real numbers and {ξt, - ∞ < t < ∞} is a doubly infinite sequence of strictly stationary ∅-mixing random variables. Under conditions on {bj,j≥} which entail that {Xt, t≥1} is either a long memory process or a linear process, we study asymptotics of Sn(s) = (Multiple line equation(s) cannot be represented in ASCII text)Xt (properly normalized). When {Xt, t≥1} is a long memory process, we establish a functional limit theorem. When {n(s) = (Multiple line equation(s) cannot be represented in ASCII text)Xt is a linear process, we not only obtain the multi-dimensional weak convergence for {Xt, t≥1}, but also weaken the moment condition on {ξt, - ∞ < t < ∞ } and the restriction on {bj,j≥0}. Finally, we give some applications of our results. [ABSTRACT FROM AUTHOR]

The Al 2 NdNi 3 single-phase alloy was prepared by melting stoichiometric Al, Nd, and Ni in the nonconsumable arc furnace filled with argon and then homogenizing annealing at a high temperature. The crystal structure of Al 2 NdNi 3 was analyzed by an X-ray powder diffraction technique, Rietveld refinement analysis, and the first-principles simulation calculation method. The results show that Al 2 NdNi 3 crystallizes in ordered variant of CaCu 5 -type structure (space group P 6 /mmm , No. 191, hP 6), and the lattice parameters of a ​= ​5.1247 (1) Å, c ​= ​4.0941 (1) Å, Z ​= ​1 and D calc ​= ​6.68 ​g/cm3. The temperature-dependent susceptibility data shows field sensitive behaviour of Al 2 NdNi 3 with low temperature transformation of magnetic ordering below ~100 ​K Al 2 NdNi 3 exhibits ferromagnetic properties at low temperatures and has a Curie temperature (T c) of 253 ​K in field of 50 ​Oe with the saturation magnetic moment (M s) of 0.25 μ B /f.u. in field of 8 ​T and coercivity (H c) of 0.16 ​T at 2 ​K. The simulation results also show that Al 2 NdNi 3 is a ferromagnet with a spontaneous magnetic moment of 0.28 μ B /f.u., and it is believed that the spontaneous magnetization of this alloy originate from the electron spin in the Nd(1a)-4f band. [Display omitted] • The Al 2 NdNi 3 ternary single-phase alloy was prepared. • Al 2 NdNi 3 crystallizes in the hexagonal cell with space group of P 6 /mmm. • Al 2 NdNi 3 exhibits ferromagnetic properties with T c of 253 ​K in field of 50 ​Oe. • The magnetic properties of Al 2 NdNi 3 originate from the electron spin in the Nd(1a)-4f band. [ABSTRACT FROM AUTHOR]

Compared with traditional alloys, high-entropy alloys (HEAs) have been widely studied because of their unique phase formation rules and excellent physical properties. This work used the first-principle calculation method to study the effect of Cr content on the phase formation, stability, and mechanical properties of MoNbTiV refractory HEAs (RHEAs). The structural model of CrxMoNbTiV (x = 0.00, 0.25, 0.50, ..., 2.00) RHEAs was constructed by the virtual crystal approximation method. The structural model was geometrically optimized using the Cambridge Sequential Total Energy Package code, and the structures' binding energy, enthalpy of formation, and elastic constants were calculated. The results show that the CrxMoNbTiV RHEAs can form a stable body-centered cubic structure, and the addition of Cr significantly impacts the lattice constant, elastic constant, plastic toughness, and elastic anisotropy of the alloy. At the same time, the three-dimensional surface map of Young's modulus anisotropy is also drawn. [ABSTRACT FROM AUTHOR]

The effect of grain size and second-phase distribution on the corrosion performance of metal inert gas-welded Al–MgSi–Mn–Cr alloy in different regions was investigated. Due to the welding heat, the transformation of the strengthening phase β″ (Mg5Si6) to B′ phase (Al3Mg9Si7) in heat-affected zone I leads to the reduction in strengthening ability of the joint. In comparison with the equiaxed grain of weld zone, base metal exhibits high grain boundary density of the fibrous grain and a large amount of second phases on the grain boundary, which increases the anodic dissolution site, leading to the highest pitting corrosion susceptibility. Additionally, the continuous distribution of β (Mg2Si) precipitate and Al(MnFe)Si dispersoid at grain boundary forms a corrosion channel, which promotes the corrosion propagation along grain boundary. [ABSTRACT FROM AUTHOR]

The Al1.5Co3.5Nd compound was prepared by means of experimental synthesis, after which the Rietveld refinement of the powder X-ray diffraction pattern (PXRD) was used to determine its crystal structure. The results show that Al1.5Co3.5Nd crystallizes in the hexagonal cell, space group of P6/mmm (No. 191) with the lattice parameters of a = 5.1041(7) Ǻ and c = 4.0576(3) Ǻ, Dcalc = 7.09 g/cm3, and Z = 1. It is isostructural with the Al2NdNi3 compound (CaCu5-type). The temperature-dependent magnetic susceptibility curve of Al1.5Co3.5Nd was obtained in the temperature range of 2–600 K, which indicates that the Curie temperature (Tc) and paramagnetic Curie temperature (Tp) are 516 K and 522 K, respectively. The effective magnetic moment μeff is estimated to be 0.53 μB. Under the applied magnetic field of up to 8 T, the saturation magnetic moment and magnetic hysteresis loop of Al1.5Co3.5Nd compound were measured at different temperatures in the temperature range of 2–300 K. The results show that the saturation magnetic moment MS is 0.082 µB/f.u., the remnant magnetization Mr is 0.035µB/f.u., the coercivity Hc is 1.59 T at 2 K, and the compound is a magnetic material within the temperature range of measurement. [ABSTRACT FROM AUTHOR]

In this article, we study semiparametric estimation for a single-index panel data model where the nonlinear link function varies among the individuals. We propose using the refined minimum average variance estimation method to estimate the parameter in the single-index. As the cross-section dimensionNand the time series dimensionTtend to infinity simultaneously, we establish asymptotic distributions for the proposed estimator. In addition, we provide a real-data example to illustrate the finite sample behavior of the proposed estimation method. [ABSTRACT FROM AUTHOR]

Abstract  We investigate the asymptotic behavior of a robust version of local linear regression estimators with variable bandwidth for spatial associated processes. The weak consistency of the proposed estimators is given under appropriate conditions. Furthermore, we establish the asymptotic normality of the estimators, from which expressions for the asymptotic bias and variance can be derived. [ABSTRACT FROM AUTHOR]

Interleukin-6 (IL-6) is a multifunctional cytokine that plays a key role in the inflammatory response. Individuals with physiological levels of IL-6 up to 200 fM may be suffering from a disease, and its rapid and accurate detection can prevent and save patients. In this work, we designed and fabricated an electronic biosensor made of an Au-functionalized wrinkle graphene field-effect transistor. The sensor was fabricated by thermally annealing gold nanoparticles formed as a substrate and transferring graphene grown by chemical vapor deposition to form deformed structures. In this structure, the nanoscale deformation could create high potential in the sensing channel, thus reducing the Debye shielding effect of the ionic solution and increasing the size of the electrical double layer, giving it a limit of detection of 1.6 × 10−15 M (33.6 fg/mL), which is significantly better than that of the bare graphene field-effect transistor sensor (4.7 × 10−14 M) and other mainstream sensors. Thus, this work provides an excellent technical tool for the label-free, accurate, and rapid monitoring of IL-6 and various biomolecules in human diseases. [Display omitted] [ABSTRACT FROM AUTHOR]

mPL100 protective layer LOBs performance enhancement principal. [Display omitted] • Cycle life of Li-O 2 battery is extended 6.2 times than the unprotected ones. • Mesoporous silica protective layer increased Li+ transport capacity, uniform Li+ flux, and inhibited Li dendrite. • Li||Li symmetric cells can operate steadily for 2,276 h while maintaining low overpotential. • Uniform distribution of cathode products generated and the overcharge potential is reduced. We are reporting a new lithium (Li) anode protective strategy using mesoporous silicon dioxide (mSiO 2) to extend the operation lifetime for Li oxygen batteries (LOBs). The fabrication method is simple and easy for mass production. The mSiO 2 protective layer inhibits the growth of Li dendrites and prevents the intrinsic Li corrosion phenomenon that occurs in all Li-metal-based batteries during charge/discharge cycles, the porous structure of nanoparticles within the mSiO 2 also actively created parallel pathways for Li+ transport thus enhancing the mass transfer. Propylene carbonate/Li with a 100 μL drop amount of mSiO 2 protective layer (mPL100) showed excellent per-formance in the Li||Li battery charge/discharge cycle test, withstanding 2,276 h of continuous operation, almost 16 times longer than the battery without the protective layer, and the LOB charge/discharge cycle test with the mSiO 2 protective layer reached 382 cycles, another significant increase of 6.2 times over the unprotected battery, the full discharge capacity also reached 56,902 mAh/g that is nearly 19 times higher than the unprotected cell. Because the mSiO 2 made Li+ flux more homogeneous meanwhile promoting the uniform distribution of positive electrode products and accelerating their decomposition, thus the overpotential is reduced. This layer also blocked any H 2 O and peroxide molecules which attack the Li anode, therefore the retention of Li has been kept at a satisfactory level. [ABSTRACT FROM AUTHOR]