Parameters C0 Research Articles

Holographic study on Einstein ring for a charged black hole in conformal gravity

Under the help of AdS/CFT correspondence, the Einstein ring of a charged black hole in conformal gravity has been studied. As an alternative to standard Einstein’s theory, the conformal gravity is both renormalizable and unitary. And such conformal theory can naturally solve some troublesome problems in physics, such as the quantum gravity problem, the dark matter problem and so on. Considering the conformal gravity has many advantages, therefore in this paper, we study Einstein ring and the effect of gravity-related parameters c0, temperature T and chemical potential u on such Einstein ring in the conformal gravity through the holographic method. Explicitly, imposing an oscillating Gauss source on one side of the AdS boundary which propagates in the bulk, the response functions are derived on the other side of the boundary. Given the proposed wave optics system, the Einstein rings are observed as expected. All these results reveal that when the observer locates at the north pole, there always exists the Einstein ring and surrounding concentric stripes. And such ring becomes into a luminosity-deformed ring or light spot while the observer departs away from the north pole. We also investigate the effect of temperature T, chemical potential u and gravity-related parameters c0 on the ring radius. We find the ring radius increases with the decrease of the temperature, increase of the chemical potential, and increase of the gravity-related parameters respectively. And more, in conformal gravity, because the charge does not appear in the metric but is given by the constraint relation, the chemical potential affects the radius of the ring differently compared with that in Einstein’s gravity. To check the results in the framework of holography, we investigate the ingoing angle of photon at the photon ring via geometric optics and find it is consistent with the angle of Einstein ring obtained via holography.

Land Use Effects on Soil Organic Carbon Fractions in the Pampas of Argentina Determined via Inverse Analysis of Carbon Mineralization Data

ABSTRACT Understanding the long-term effects of land use on soil organic carbon (SOC) is of interest due to the possibility of using soils as carbon (C) sinks. We determined how the C levels in the labile (C0) and stable (Cr) fractions of the organic matter of soils of the Pampas were affected by land use, applying the inverse analysis methodology. Soil samples (0–50 cm) taken from 198 sites under different land uses (forest, grassland, and cultivated soil) were incubated for six months, adjusting a one pool exponential model to C mineralization data. The model allowed a good estimation of C0 (R2 = 0.96–0.98) and Cr was calculated by difference with SOC. The stocks of C0 and Cr were calculated on an equivalent mass basis (5000 t soil ha−1) for comparisons. About 80% of the SOC is found in the Cr pool in the Pampas soils. Both C0 and Cr were higher in forest and grassland than in cultivated soils. Soil cultivation produced an average reduction of 30% of the C0 pool and 18% of the Cr pool in relation to grassland soils. Consequently, most of the SOC lost due to agriculture came from the Cr pool. In mixed production systems, no differences of the C pools were detected between the crop or pasture phase of the rotation. The parameters C0 and k (mineralization rate constant) of the exponential model were negatively correlated, so the comparison of C0 between different land uses must be careful. The k parameter was similar among land uses when the comparison was made for similar values of C0, suggesting that land use affected the size of the C pools not their stability. Forest and grasslands were effective systems for sequestering C in stable forms. Expanding the cultivated area in the Pampas should be avoided if it is desired not to emit C into the atmosphere from the soil.

Investigating the effect of food trays porosity on the drying process

Improving the dynamics of airflow behavior in the drying system is essential for improving its efficiency. In this study, the impact of the porosity of the plum-filled drying trays on the airflow behavior inside the chamber is investigated. Three configurations of constant porosity (30%, 50%, 70%), and 4 additional configurations of porosity varying linearly along trays 1 and 2 as a function of the x-axis are evaluated. To model the trays as a porous medium, the Power-low mathematical model was used, with the built-in parameters C0 and C1 were given experimentally. Both were formed as a subroutine in C++ and integrated into the CFD code, as was done for the integration of unsteady weather parameters. The CFD method used for visualizing and analyzing the airflow behavior in the drying chamber has been previously validated with the experimental result. The obtained results indicate that the average temperature of the drying trays reaches a maximum of 316,91 k at tray 1 for a porosity of 50% and an inlet velocity of 0.1 m/s. For the case of non-uniform trays porosity, it was found that for low air inlet velocity of 0.1 m/s, the velocity difference ΔV is generally low in cases (b) and (d), where it is equal respectively to 0.02 m/s and 0.01 m/s for tray 1, and 0.01 in both cases for tray 2. While for high air inlet velocity of 1 m/s the difference ΔV in both trays reaches 0.17 m/s.

МАГНИТОМЕТРИЧЕСКИЙ ПОДХОД К ИЗУЧЕНИЮ ВЛИЯНИЯ УГЛЕРОДА И АЗОТА НА КОРРОЗИОННУЮ СТОЙКОСТЬ АУСТЕНИТНЫХ ХРОМОНИКЕЛЕВЫХ СТАЛЕЙ

Extensive investigations of the corrosion resistance of austenitic chromium-nickel steels (with stable and unstable austenite) do not reveal the full nature of the phenomena studied. By the magnetometric method, using the specific magnetic susceptibility c0 of austenite and low content Рd d-ferrite (0…0.5 %), the corrosion properties of steels were studied. These parameters are determined by the chemical composition of steel, heat treatment and deformation. The pitting corrosion rate correlates with these parameters. In the scientific literature, the results of studies of the corrosion resistance of metals with variation over a wide range of carbon and nitrogen contents are given. In this work, the influence of these elements in five swimming trunks of each steel grade AISI 304 and AISI 321 is investigated. In steels AISI 304 and AISI 321, with an increase in c0 and Рd (under the condition low content of d-ferrite), the pitting corrosion rate К decreases. The values of c0 and Рd from the total content Q (wt.%) of all alloying elements for each melt are determined. The dependences of the corrosion rate K on Q for each melt of AISI 304 and AISI 321 steels are obtained. Various forms of К(Q) dependency curves are caused by a lower content of nickel, silicon and a higher content of chromium, manganese, carbon, nitrogen in AISI 304 steel. Also in AISI 321 steel, titanium is present. The dependences of К, c0 and Рd on the content of carbon and nitrogen, in the presence of all other alloying elements, are determined. Various (presence of a maximum or minimum) parabolic dependencies of К, c0 and Рd on the carbon and nitrogen contents were revealed. For melts of steel AISI 304, of the effect carbon and nitrogen on corrosion resistance are the opposite of the sum of all other alloying elements. For melts of steel AISI 321, the carbon present makes a smaller contribution to corrosion resistance compared to nitrogen. For AISI 321 steel melts, an ambiguous character of the dependence of the pitting corrosion rate K on the total content Q of all other alloying elements was found. Two values of Q (a parabolic dependence of K(Q)) can correspond to one value of K. The critical points (the content of alloying elements), before and after which the parameters c0, Pd can increase or decrease, are determined. As a result, the paramagnetic state of austenite changes, which correlates with a change in the corrosion properties of steel.

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives.

Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (nij) with a considerable combination of assay conditions (cj). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of cj. They are focused on >500 different biological activity parameters (c0), >340 different targets (c1), >6200 types of cell (c2), >120 organisms of assay (c3), and >60 assay strains (c4). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), näive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.

Evolution of the mechanical properties of a medical device regarding implantation time

BackgroundOur study aimed at understanding the influence of healing time on the mechanical properties of meshes used in pelvic organ prolapse, once implanted in an animal model using the rat. MethodsA standard polypropylene mesh was implanted in 42 rats in order to evaluate the mechanical properties of the implanted mesh. Explantation occurred at 1, 2, 3, 4 and 5 months and mechanical tests were performed. Each sample was mechanically evaluated by a uniaxial tensile test with a machine (BIOTENS). Biological tissues presented a nonlinear relation between stress and strain so it could be modeled by the 2 parameters C0 and C1 of a second-order Mooney-Rivlin law. ResultsThe rigidity in small deformation might not be affected by healing time or the presence of the synthetic implant. On the contrary, changes seemed to occur on the stiffness in large deformation (C1). The stiffness with the mesh composite changed with healing time. The “two-month implantation” rat group was significantly more rigid than the two control groups (pcontrol/2months = 0,04 and pplacebo/2months = 0,04). The 2- and 3-month healing groups were significantly more rigid than the 1-month healing group (p1/2months = 0,01 and p1/3months = 0,003). After 2 months, the mechanical properties seemed to stabilize (p2/3months = 0,44, p2/5months = 0,16 et p3/5months = 0,3). ConclusionIn order to evaluate the mechanical properties of an implanted mesh, the optimal time for explantation seems to be 2 months. Once this period is over, a more physiological mesh will be developed in order to be similar to native vaginal tissue once implanted and colonized by scar tissue.

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c0), 55 protein accessions (c1), 83 cell lines (c2), 64 organisms of assay (c3), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

Equation of State for Shocked Fe‐8.6 wt% Si up to 240 GPa and 4,670 K

AbstractUsing dynamic compression technique, the equation of state for Fe‐8.6 wt% Si was measured up to 240 GPa and 4,670 K. A least squares fit to the experimental data yields the Hugoniot parameters C0 = 4.603±0.101 km/s and λ = 1.505±0.037 with initial density ρ0=7.386±0.021 g/cm3. Based on the Hugoniot data, the calculated isothermal equation of state is consistent with static compression data when the lattice Grüneisen parameter γl =1.65(7.578/ρ) and electronic Grüneisen parameter γe=1.83. The calculated pressure‐density data at 300 K were fitted to a third‐order Birch‐Murnaghan equation of state with zero pressure the parameters K0=192.1±6.3 GPa, =4.71±0.27 with fixed ρ0ε =7.578±0.050 g/cm3. Under the conditions of Earth's core, the densities of Fe‐8.6±2.0 wt% Si and Fe‐3.8±2.9 wt% Si agree with preliminary reference Earth mode (PREM) data of the outer and the inner core, respectively. These are the upper limits for Si in the core assuming Si is the only light element. Simultaneously considering the geophysical and geochemical constraints for a Si‐S‐bearing core, the outer core may contain 3.8±2.9 wt% Si and 5.6±3.0 wt% S.

Bound chloride ingress in alkali activated concrete

Alkali activated cementitious material (AACM) concrete and conventional concrete specimens of similar strength, developed for structural applications, were exposed to a 5% NaCl solution. Bound chloride concentrations (water and acid soluble) were determined up to 270 days of chloride exposure. Chloride diffusion profiles with depth and diffusion parameters C0 and Dc were derived from the data for water soluble, acid soluble and total bound chloride concentrations in order to develop long term chloride prediction relationships. The results show that the practice of using acid soluble chloride data for long term chloride predictions in conventional concrete is not valid for AACMs due to their low chemical binding capacity. Instead the physically bound chloride (water soluble) is more predominant in AACMs and is suitable for the chloride prediction models. Therefore, relationships of chloride diffusion parameters C0 and Dc with time have been derived for water soluble chloride. These correlate with total bound chlorides and are suitable for long term predictions of chloride concentrations for the design and maintenance of AACM concrete structures against corrosion.

Vertical dynamics of soil organic carbon and its influence on Chengdu Plain paddy soils from the 1980s to the 2010s

ABSTRACTThe vertical dynamics of paddy soil organic carbon (SOC) play an important role in soil quality and carbon cycling. In this study, we used an exponential decay function to estimate the vertical dynamics of SOC content and determined its influencing factors in a typical paddy soil area of the Chengdu Plain from the 1980s to the 2010s. Our results show an overall increase in SOC stocks at 0–100 cm from 11.8 Tg in the 1980s to 13.7 Tg in the 2010s. SOC content increased at depths of 0–40 cm and declined at depths of 40–100 cm over the past three decades. The exponential decay function parameters C0 and k significantly increased by 31.4% and 18.2% respectively, which suggests the vertical pattern of SOC distribution changed. The increase in nugget effects of C0 and k and the decrease in the relative contributions of the parent material, subgroup and distance-to-river indicate that extrinsic factors played increasing roles in the vertical variation of SOC content. Our study concludes that rice planting has led to vertical variations of SOC content and decreased the effects of intrinsic factors on the vertical variation of SOC content of Chengdu Plain paddy soils over the past three decades.

Note on nonparametric spectral analysis of wideband spectrum with missing data via sample-and-hold interpolation and deconvolution

In [1] a procedure for bias-free estimation of the autocorrelation function is introduced for equidistantly sampled data with randomly occurring samples being invalid. The method incorporates sample-and-hold interpolation of the missing data points. The occurring dynamic error of the primary estimate of the correlation function is treated by a deconvolution procedure with two parameters c0 and c1 with c0+2c1=1, which are the on-diagonal and the aside-diagonal parameters of a specific correction matrix (at all lag times except zero). The parameters c0 and c1 were obtained as a function of the probability α of a sample to be valid by numerical simulation. However, explicit expressions for the parameters c0(α)=1−2α+2α2 and c1(α)=1α−1α2 can be derived, which might improve the usability of the deconvolution procedure in [1].

Grouting Reinforcement Technique in Wind Oxidation Zone by Power Law Superfine Cement Slurry Considering the Time‐Varying Rheological Parameters

With the recovery of the wind oxidation zone in 13116 working face of Gubei Coal Mine as the engineering background, the occurrence condition of coal seam and the bedrock wind oxidation zone characteristics were analyzed. On this basis, the time‐varying rheological parameters of superfine cement slurry and the permeability characteristic parameters of roof strata in wind oxidation zone were measured; then, a grout diffusion equation was established with the parameters obtained previously for the permeability characteristics of roof strata in the wind oxidation zone of Gubei Coal Mine. The grouting design was applied in actual engineering projects and its engineering effect has been proved to be satisfactory. The results indicate that superfine cement slurry whose water‐cement ratio is 0.6 is a typical power‐law slurry consistent with time‐varying rheological parameters. It has better rheological properties during pumpable period, and its rheological parameters can be controlled by highly dispersive nano‐SiO2 and polycarboxylate superplasticizer. For grouting in wind oxidized zone, comprehensive consideration must be given to effective permeability Ke, porosity ∅ , and time‐varying rheological parameters c0, k, and n. To realize effective diffusion of grouting in coal and rock mass, grouting pressure and diffusion time must be reasonably designed rather than excessively increased.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer.

Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c j formed from >70 different biological activity parameters ( c0), >300 different drug targets ( c1), >230 cell lines ( c2), and 5 organisms of assay ( c3) or organisms of the target ( c4). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

Network model evaluation of the effects of pore and particle size distributions on dynamic straining-dominant deep bed filtration in porous media

A straining-dominant filtration model based on a 2D square network is proposed. In this model, the colloid particle can be captured in a node only. The effects of network parameters (node size distribution parameters μn and σn and pore throat size distribution parameters μn and σb), particle concentration of influent (C0), and particle size distribution parameters (μr and σr) on straining-dominant deep bed filtration simulation are investigated. Four parameters (C0, μn, μb, and μr) significantly affect the simulated pressure drop and the normalized particle concentrations of effluent (Ce/C0). The increase in parameters μn and μb decreases pressure drop and increases Ce/C0, whereas the increase in parameters μr and C0 increases pressure drop and decreases Ce/C0. With the experimental condition and a 5% variation in parameters, parameters σn, σb, and σr are lowly sensitive to Ce/C0 and pressure drop. When the variation in σr is sufficiently large, the model simulation is significantly affected. The simulated normalized effluent concentrations and pressure drop are consistent with the experimental data under appropriate simulated conditions.

Spectra shifts of a chirped Gaussian pulsed beam propagating in slant atmosphere path

Based on the extended Huygens-Fresnel principle, the cross-spectral density function of a chirped Gaussian pulsed beam propagating in slant atmosphere path was derived, and the spectra shift properties of chirped Gaussian pulsed beam in slant atmosphere path were studied. The results show that the on-axis spectral shifts may be red shift comparing to the spectra at the source plane L = 0, while the off-axis spectra shifts is blue shift. The results also show that the red shift or the blue shift of chirped Gaussian pulsed beam in slant atmosphere path will increase as the parameters C0 and α of slant atmosphere increase.

Reform of the drift-flux model of multiphase flow in pipes, wellbores, and reactor vessels

To aid the understanding of multiphase flows and development of associated computational simulations, we reappraise the drift-flux model to improve accuracy and physical understanding. New insights are supported by careful re-derivation, literature review, and empirical verification. We directly calculate C0 and C1 using simultaneous measurements of instantaneous gas fraction and gas-liquid velocities, all obtained from a microscopic measurement volume (∼1 mm3) inside a macroscopic gas-liquid pipe flow (∼5000 cm3) containing bubbles of ~250 μm diameter. A reformulated drift-flux plot is created for this macroscopic flow by using γ-beam densitometry and total flow measurements. The microscopic and macroscopic data both confirm that significant error exists in common methods and assumptions regarding the drift-flux model, namely in the common assumption that the drift-flux parameters C0 and C1 are invariant with respect to changes of flow. Our reformulated drift-flux model finds greatly different values of C0 and C1 from those predicted by standard drift flux methods, and finds values of C0 and C1 to undergo a regime transition due to changing bubble dynamics in the multiphase flow, wherein at low gas flow the bubbles seldom interact, as if they are at infinite dilution, while above a critical gas flow the hydrodynamics change due to bubbles interacting significantly with each other. These insights are uncovered only by reformulation of the practices of the drift-flux model.

Effects of Anthropogenic Disturbance on Sediment Organic Carbon Mineralization Under Different Water Conditions in Coastal Wetland of a Subtropical Estuary

The changes in soil organic carbon (C) mineralization as affected by anthropogenic disturbance directly determine the role of soils as C source or sink in the global C budget. The objectives of this study were to investigate the effects of anthropogenic disturbance (aquaculture pond, pollutant discharge and agricultural activity) on soil organic C mineralization under different water conditions in the Minjiang River estuary wetland, Southeast China. The results showed that the organic C mineralization in the wetland soils was significantly affected by human disturbance and water conditions (P aquaculture pond sediment > soil near the discharge outlet > rice paddy soil. This indicated that human disturbance inhibited the mineralization of C in soils of the Minjiang River estuary wetland, and the inhibition increased with the intensity of human disturbance. The data for cumulative mineralized CO2-C showed a good fit (R2 > 0.91) to the first-order kinetic model C t = C0 (1–exp(–kt)). The kinetic parameters C0, k and C0k were significantly affected by human disturbance and water conditions. In addition, the total amount of mineralized C (in 49 d) was positively related to C0, C0k and electrical conductivity of soils. These findings indicated that anthropogenic disturbance suppressed the organic C mineralization potential in subtropical coastal wetland soils, and changes of water pattern as affected by human activities in the future would have a strong influence on C cycling in the subtropical estuarine wetlands.

Stress-free deposition of [001] preferentially oriented titanium thin film by Kaufman ion-beam source

We proposed a method to control and minimize residual stress in [001] preferentially oriented Ti thin films deposited by a Kaufman ion-beam source using a substrate temperature during deposition (T) as the parameter. We determined the residual stress, corresponding lattice parameters, and thickness of deposited films using X-ray diffraction and X-ray reflectivity measurements. We showed that the Ti film deposited at T≈273°C was stress-free with corresponding lattice parameters a0 and c0 of (2.954±0.003)Å and (4.695±0.001)Å, respectively. The stress-free sample has the superior crystallographic quality and pure [001] orientation. The Ti thin films were oriented with the c-axis parallel to the surface normal. We also investigated root mean square of surface roughness of deposited films by atomic force microscopy and it was in the range from ≈0.58nm to ≈0.71nm. Such smooth and stress-free layers are suitable for microelectromechanical systems.

Predicting VOC emissions from materials in vehicle cabins: Determination of the key parameters and the influence of environmental factors

Emission of volatile organic compounds (VOCs) from materials in vehicle cabins is one of the main reasons for poor in-cabin air quality. The emission behaviors can be characterized by key parameters including the initial emittable concentration (C0) and the diffusion coefficient (Dm). Determination of these parameters provides a foundation for predicting emissions and evaluating driver and passenger exposures. By analyzing VOC emissions from in-cabin materials under ventilated conditions, we propose a novel method to simultaneously determine C0 and Dm. This method initially establishes a linear correlation between the logarithm of excess VOC concentration and emission time, then the key parameters C0 and Dm are obtained by linear curve fitting and solving two equations. Using this method we determined the C0 and Dm for five VOCs (benzene, toluene, p-xylene, ethylbenzene and styrene) emitted from a common in-cabin material under different environmental conditions. Good agreement between the predicted VOC concentrations based on the measured key parameters and experimental data demonstrates the effectiveness of this method. Moreover, the impact of temperature on C0 was investigated for the five VOCs. Results indicated that C0 of all the studied VOCs increased with an increase in temperature, and toluene showed the greatest increase. When the temperature increased from 25°C to 50°C at the relative humidity of 50%, the C0 of toluene increased by 1.07 fold.

Multichannel quartz crystal microbalance array: Fabrication, evaluation, application in biomarker detection

A multichannel quartz crystal microbalance array (MQCM) with three pairs of gold electrodes was fabricated for detection of two biomarkers: acetone and nitric oxide (NO). The gold electrodes were deposited symmetrically on an AT-cut 10 MHz circular quartz plate using photolithography, sputtering, and lift-off technologies. The effect of gold layer thickness on MQCM performance was investigated and the optimized thickness was 101 nm. The simulation values of the electric parameters C0, Cq, Lq, and Rq in the Butterworth–Van Dike equivalent circuit for the MQCM device were 97 pF, 1.3 pF, 1.05 mH, and 9.8 Ω, respectively. Simulation values were in the theoretical range, which indicated that the fabricated MQCM device had good resonance performance. Two types of nanocomposites, titanium dioxide–multiwalled carbon nanotubes and cobalt (II)phthalocyanine–silica, were synthesized as sensing materials. The sensing mechanism is based on coordination adsorption of target molecules onto the sensing material, resulting in a resonant frequency shift of modified QCM sensor. A linear range from 4.33 to 129.75 ppmv for acetone was obtained and one from 5.75 to 103.45 ppbv for NO.