Specific exfoliation yield (fifth column) for graphene produced using graphite rod electrodes in different IL and DES samples in acetonitrile as the solvent (1:1:30 by volume mainly for DESs when mixed with IL and ACN – the ILs on their own were only dissolved in ACN).DES/IL category (all DESs were mixed with BMPyrrBTA and ACN)Average current applied (A)Energy consumed per unit weight of rod (kJ/g)aYield of graphene (%)bGraphene yield per unit Cy7 NHS ester consumed (g/kJ)Colour of solution after exfoliationType I DESb0.1658.340.00.0480Pale yellowType II DES0.0313.10.00.0000BlackType III DES (ammonium-alcohol)0.0737.40.10.0001Deep yellow/brownishType III DES (phosphonium-alcohol)0.0949.528.80.0303Dark brownType III DES (ammonium-acid)0.0787.915.00.0190Light brownType III DES (glycerol-phosphonium)0.0717.228.40.0394Light yellowType III DES (NADES)0.0262.68.00.0308Pale yellowType III DES (ammonium-amide)0.0868.728.00.0322Dark brownType III DES (phosphonium-amide)0.0858.632.00.0372Dark brown to blackType IV DES0.1005.148.70.0963Pale yellowBMiMBF4 (no DES)c0.0636.416.00.0250OrangeBMPyrrBTA (no DES)c0.0414.217.30.0412ColourlessaEach graphite rod weighed 1 g prior to exfoliation. For the case of Type I and Type IV DESs, two graphite rods were used so both weights were considered in the energy and specific yield calculations.bYield of graphene was estimated by dividing the weight of the nanomaterial produced by the original weight of the graphite rod prior to exfoliation (for Types I and IV DESs, weights of two graphite rod electrodes were taken into account for each case, respectively).cThese ILs were dissolved in ACN at 1:30 volume ratios on a separate basis. No other components (DESs) were used.Full-size tableTable optionsView in workspaceDownload as CSV
The above set of ordinary differential equations (ODEs) for three independent variables, viz. X, G and E, and the parameters therein characterize the S peptide process. These equations have total of 10 parameters, viz. K3, K1, K3E, kd, μm, a, b, YX/G, m and K4. In order to get the physical insight into the ultrasound induced enhancement of fermentation, the numerical solution of above equations was compared with the experimental time profiles of X, G and E. The unknowns in this model are the kinetic and physiological parameters, whose optimum values need to be determined, so as to match the time profiles of the substrate, cell mass and ethanol concentrations calculated using the model with the experimental data. As per the findings of Philippidis et al. (1992), null value has been assigned to the constant K4. The main model of three ordinary differential equations was solved using Runge–Kutta 4th order method, and optimization of the parameters was done by calculating root mean square (RMS) error between experimental and model results using Genetic Algorithm (GA). The objective function (Obj) for the optimization was defined as follows:Obj = min∑i=1neri where n is the number of experimental data points for glucose concentration, ethanol concentration and cell mass concentration. The error (er) is defined as:eri=Giexp-Gipred2+Xiexp-Xipred2+Eiexp-Eipred21/2
2.2.3. Step 3: microwave pretreatment (MP)
2.2.4. Step 4: microorganism decomposition (MD)
2.2.5. Step 5: ammonia immersion (AI)
The residual solids from Step 4 (Solid residue AN1 and AN2, Solid residue TR1 and TR2, and Solid residue PJ1 and PJ2) were immersed in a beaker containing an ammonia solution, with a mass ratio of 2:1 for ammonia to sugarcane. The beakers were subjected to autoclave at 90 °C for 30 min. Then the LY2886721 of the filtered hydrolysis solution was neutralized to 7.0 with 1% H2SO4 and retained for fermentation experiments. The residual solids (Solid residue AAN, ATR, and APJ) were dried at 65 °C and their solid loads were determined. These samples were then used in the next step of decomposition. After Solid residue AN2, TR2, and PJ2 were pretreated by Step 5, the solid–liquid mixtures were neutralized and kept as Solid–liquid mixture AAN, ATR, and APJ, respectively.
2.2.6. Step 6: enzymatic hydrolysis (EH)
2.3. Microorganism and inoculum preparation
Biogas production was analyzed in the presence of four different Obeticholic Acid (NS, BES, CES and AHX) together with 10 mM sodium acetate as substrate (Fig. 3). The following concentrations were examined for NS, BES, CES, AHX: 0.55, 20, 20, 1.82 mM, respectively. AHX was prepared as stock solution (10 mg L−1 in 1 M NaOH). All experiments were performed in duplicate.
Voltages were used to calculate the current density (A /m2) according to P = IV/A, where I was the current, V was voltage, and A was cross-sectional area of anode (n = 2). Coulombic efficiency was calculated as CE = CP/CTi × 100%, where CP was the total Coulombs calculated by integrating the current over time and CTi was the theoretical amount of coulombs based on added substrates.
The observed biogas production and the cumulative gas volumes for each gas fraction was calculated as previously described (Logan et al., 2002). The expected biogas production was calculated as VE,t = CtV/2F, where, VE,t (mL) is electrostatic attraction the expected specific biogas production at sample time t based on integrated current over time in Coulombs (C), F Faraday’s number (96.485 C/mol), Ct the total Coulombs by integrating the current over time, and VM the molar gas volume (25.200 mL/mol at 30 °C). The cathodic hydrogen recovery was obtained from the ration of Vt over VE,t. The overall hydrogen recovery (RH2) was calculated using the following equation, RH2 = CERCAT. The hydrogen yield of the system (YH2) was the amount of hydrogen generated based on total substrate used ( Hu et al., 2008).
The same dimensions and pressure drop as that of a straight tube should have same mass flow rate through bend tube if the flow is fully attached without any secondary flow. Therefore, lower mass flow rate through the bend tube indicates that the flow is with secondary flow due to bend and has to overcome additional resistance due to secondary flow causing the LDC1267 in mass flow rate. This conclusion (on the basis of measurements and analytical solution) further reinforces the numerically obtained streamlines and velocity vector plots showing the secondary flows (Fig. 11).
In this work, experiments for low pressure nitrogen gas flowing through three different bend test sections of diameters (8, 13 and 20 mm) and lengths (208, 213 and 220 mm) are performed. The measurements cover part of the continuum and slip flow regimes (0.0003 < Kn < 0.0385; 0.27 < Re < 418.5). The static pressure measurements along the inner, outer and top walls are undertaken and analyzed to understand the flow behavior. The results are compared with analytical solution obtained for flow in a straight tube under similar flow conditions. The velocity field is thermiogenesis obtained through numerical analysis for better understanding of the flow behavior near the bend.
Cleaner production designs and delivers strategic solutions for green chemicals, sustainable materials and environmentally preferable products (Ozalp et al., 2010). The reduction of CO2 emission is thought to be the primary aspect of cleaner production (Benhelal et?al., 2013 and Vet?né Mózner, 2013). Recent studies have attempted to explore several ways to reduce CO2 emission (Tonn et al., 2014), including readjusting industrial structures by the expansion of enterprises with low emission and low Cy5 hydrazide consumption, the exploration of new, efficient, and clean industrial energy, the capture and storage of CO2 for industry demand, and the alternation of CO2 to valuable chemicals. The last strategy involving both the consumption of greenhouse gas CO2 and the production of value-added chemicals (Aresta and Dibenedetto, 2007, Dong et?al., 2009, He et?al., 2009, He et?al., 2006 and Hunt et?al., 2010) presents great economic and environmental interests (Van-Dal and Bouallou, 2013). The direct formation of dimethyl carbonate (DMC) from CO2 and methanol is one of the promising reactions for substitution purpose (Delledonne et?al., 2001 and Sakakura and Kohno, 2009). DMC is an important green chemical substitute for corrosive and toxic carbonylating and methylating agents, such as dimethyl sulfate and phosgene. It is also considered as an environment friendly intermediate for higher carbonates and carbamates as well as a promising octane enhancer (Ono, 1997, Pacheco and Marshall, 1997 and Tundo and Selva, 2002).
This review summarizes the chemical characteristics and environmental impact of LMP. As a low-cost absorbent or agent, the current progresses on LMP potential applications such as wastewater and air pollution, remediation of contaminated soils, and bio-treatment of organic waste are also reviewed in details.
2. Production and characteristics of LMP
2.1. LMP production
Pulp and paper plants are primary sources of LMP in the causticization process versus mechanical, chemi-mechanical and chemical methods, respectively. Kraft process, the most dominating chemical pulp, is used to dispose of wood Bikinin in spite of pollution problems posed by malodorous compounds. The flow sheet of LMP production in kraft pulp papermaking process has been shown by Martins et al. (2007), together with Eq. (1) (Cheng et al., 2009), as seen from Table 3.
In general, precipitated calcium carbonate (PCC) is generally produced from mined, crushed calcium carbonate (CaCO3) and calcium silicates, with a higher purity of CaCO3 than that of ground CaCO3 (GCC) from a controlled synthesis through calcination and hydratization, filtration and carbonation. There are several various types of PCC grades, but the purity of PCC is usually over 99 wt% with density of 2700 kg/m3 (Teir et al., 2005). In papermaking process, PCC could enhance paper bulk, brightness, light scattering and printability as a filler agent. However, the calcination of CaCO3 requires about the external heat of 2669 kJ/kg CaCO3 (with an initial temperature of 25 °C) at 900 °C (Teir et al., 2005), Consequently, it causes a high cost and energy consumption.