Recovery of hydrogen from hydrogen sulfide by indirect electrolysis process

Recovery hydrogen from hydrogen sulfide is an effective way of utilizing exhaust gas. In this paper, removal of hydrogen sulfide by indirect electrochemical process was studied using acidic aqueous solution of Fe³⁺/Fe²⁺ as the electrochemical intermediate. Solid polymer electrolyte was applied to hydrogen production by indirect electrolysis of H₂S, in which the anode was graphite cloth, the cathode was the platinized graphite cloth, and the membrane was proton exchange membrane. The results of electrolysis experiments showed the relationship of current density as a function of electrolytic voltage at constant flow rate of electrolyte, temperature, and electrolyte composition. The effect of the cathode liquid velocity on current density was small. When the flow rate of anode electrolyte was greater than 200 L/hr., the current density tended to be stable. When [Fe³⁺]>0.20 mol/L, the concentrations of Fe²⁺ and Fe³⁺ ions in the anode solution had no significant impact on the current density. The current density gradually increased with temperature. In the electrolytic process of hydrogen production, the Fe²⁺ ions diffused from the anode to the cathode. The amount of diffusing Fe²⁺ ions gradually increased with time. The effect of Fe²⁺ ions diffusion from anode to cathode on hydrogen production was discussed.     

Vanadium carbide coating as hydrogen permeation barrier: A DFT study

Density functional calculations are used to investigate hydrogen (H) behaviors in vanadium carbide (VC). Molecular H₂ dissociation, atomic H diffusion and penetration are analyzed using the transition state theory. H₂ prefers to be close to the surface as physical adsorption, providing an environment conducive for further dissociation, and dissociates into atomic H adsorbed at the top C atom sites with co-adsorption state. The dissociation rate on the surface is mainly limited by the temperature-controlled activation energy barrier. The adsorptivity of atomic H by the surface tends to decrease as increasing of H coverage. For atomic H penetration through the surface, a significantly endothermic energy barrier and the low diffusion prefactor suggest that the main resistant effect of H permeation takes place at the surface. Energetic, vibrational, electronic consequences, and quantum effects on the H behaviors are discussed thoroughly. Our theoretical investigation indicates VC is a promising hydrogen permeation barrier.     

Effect of hydrogen enrichment and TiO2 nanoparticles on waste cooking palm biodiesel run CRDI engine

In this study, we deal with the production and utilization of waste-cooking palm biodiesel (WCB) and.evaluated the influence of the addition of titanium dioxide (TiO₂) nanoparticles in hydrogen-enriched single-cylinder CRDI diesel engine. XRD, SEM, and EDX decide the structure and morphology of TiO₂ nanoparticles. The TiO₂ nanoparticles were dispersed in the tested fuels at a dosage of 50–75 ppm with the aid of ultra-sonication. Based on the oxidation stability study, the B20 + 75 ppm (TiO₂) fuel blend is the pilot fuel for the engine test. Further, the engine is enriched with a hydrogen (H₂) flow of 10 lpm. Results revealed that the performance parameters were improved with the addition of H₂ enrichment and TiO₂ nanoparticles compared to D. The brake thermal efficiency of the engine was improved by 8.21%. In comparison, brake-specific fuel consumption decreased by 42.86%. Furthermore, adding nanoparticles also reduced CO and HC emissions by 74% and 27.27%, respectively, whereas the NO_x emission was slightly increased. Thus, the findings demonstrated that hydrogen-enriched nanoparticles added to biodiesel might be considered a substitute for fossil fuels and report a positive impact on diesel engine performance without requiring significant modifications.     

Signal characteristic and test exploitation for intermittent nanometer-scale cracks

This paper analyzes signal distortion caused by nanometer-scale solder ball fractures. A solder ball fracture causes an intermittent open circuit on the transmission line. The resulting basic failure mechanism is a drop in signal voltage, due to the capacitance-induced Alternating Current (AC)-coupling effect (which is induced by the fractured solder ball). The two major contributing factors to this error are fracture height and the persisting duration of the consecutive same-logic-value signal. The required signal that induces a voltage drop, sufficient to detect nanometer-scale solder ball fractures, can be composed by repetition of certain signal patterns even for the I/O connections with run-length restrictions. The methodology is newly proposed to determine potential ranges of solder ball fractures. Test pattern generation is made by maximally exploiting the compounding effect of various sizes of same data bits to generate effective run-length that is larger than maximum run length for the purpose of detecting intermittent solder ball fractures. In DDR3 memory systems with 5-nm solder ball fractures, at least 29 bits of consecutive logic “1” or “0” signals are required to detect fractures. If the system has a maximum run-length of 10, 20, or 30 bits, the test signal—which has the equivalent voltage-dropping effect as 29-consecutive bits—can be generated with six, two, or one repetition of the test pattern, respectively; the test pattern is in the form of concatenated N-1 bits of consecutive logic “1” and 1 bit of logic “0” where N is the maximum run length. In addition, a SPICE simulation was conducted to confirm correlation between calculations and actual results. In the simulation, in order to detect a 5-nm solder ball fracture, at least 37 bits of consecutive signal were required.     

Improved CCM for variable causality detection in complex systems

Convergent cross-mapping (CCM), has been largely implemented for variable causality detection in complex systems like chemical process. However, this method is susceptible to problems regarding parameter selection and threshold determination. The synchronization phenomenon and the Moran effect, which are two interference terms in causality detection, must also be addressed. Therefore, an improved CCM is proposed to overcome these limitations in this paper. In the improved CCM, the optimal embedding dimension is selected based on the pseudo-nearest-neighbor theory. Also, Monte Carlo simulation is adopted to evaluate the convergence threshold. Next, by using the defined time delay detection function, the synchronization phenomenon and the Moran effect are identified to reduce the interference terms and further improve the accuracy. Finally, the improved CCM method is applied in a numerical example and a hydrocracking process to demonstrate its feasibility and superior performance than other methods.     

Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis – A study on effect of image magnification

The scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, non-reproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.     

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods.     

Analysis of nitrogen oxide emissions from modern vehicles using hydrogen or other natural and synthetic fuels in combustion chamber

The paper presents a calculated analysis of the equilibrium emission of nitrogen oxides on the exhaust of carburetor and diesel internal combustion engines. The temperature of fuel oxidation is assumed to be 1,400 °C while the pressure for carburetor and diesel engines is assumed to be 60 atm and 80 atm respectively. The studies have been carried out for natural and synthetic fuels such as hydrogen, ethanol, methanol, petroleum, diesel fuel and methane at the excess air coefficient corresponding to the fuel oxidation temperature of 1,400 °C. In the paper, the method for calculating the equilibrium composition based on the equilibrium constant and mass conservation equations has been applied. It is shown that with an increase in pressure from 1 atm to 60 atm for carburetor engines and up to 80 atm for diesel engines, the reaction of nitrogen dioxide formation may shift towards an increase in NO₂. The formation of NO may be not affected by the increase in pressure by virtue of the fact that the reaction proceeds without changes in the amount. It has been determined that NO is the major atmospheric pollutant. However, it would be advisable to use more extensively the fuels characterized by the lowest output of nitrogen dioxide (methane and methanol), since nitrogen dioxide (NO₂) related to the 2nd hazard class is appeared to be the most dangerous to humans. It has been revealed that the reduction in oxidation temperature using hydrogen as a fuel for electrochemical current generators may allow reducing nitrogen oxide emissions by more than an order of magnitude as compared to the best results for ICE.     

A comparative study on the catalytic activities and stabilities of atomic-layered platinum on dispersed Ti0.9Cu0.1N nanoparticles supported by N-doped carbon nanotubes (N-CNTs) and reduced graphene oxide (N-rGO)

In our previous research, titanium-based nitride with high conductivity and superior corrosion resistance were developed as an ideal core material for replacing noble metal to form Pt-based core-shell catalysts by pulse electrodeposition. Meanwhile, the smaller sizes of nitride cores would also be available for pulse electrodeposition by dispersing them on carbon nanotubes (CNT). To achieve a better practice on the preparation of the Pt-based core-shell catalysts, in this work, both nitrogen-doped carbon nanotubes (N-CNT) and reduced graphene oxide (N-rGO) were used to support the copper-doped titanium nitride (Ti_0.9Cu_0.1N) cores. In the course of pulse electrodeposition, their influences as supports on the electronic states of electrodeposited Pt as well as their catalytic activities were compared. The results showed that the Pt preferred to electrodeposit on Ti_0.9Cu_0.1N cores supported by N-CNT and formed a core-shell structure. While with the same electrodeposition process, the Pt was found to be electrodeposited not only on the Ti_0.9Cu_0.1N cores supported by N-rGO with heavy aggregations but also on the N-rGO support. Raman spectroscopy analysis indicated that the higher degree of structural defects on N-rGO, as support, might have contributed to such divergence observation.     

Palladium nanoparticles supported by three-dimensional freestanding electrodes for high-performance methanol electro-oxidation

Palladium (Pd) nanoparticles (NPs) prepared by gas phase cluster deposition demonstrated excellent electrocatalytic activity. Herein, a series of Pd NPs modified freestanding electrodes with a super clean surface and easy repeating process for methanol oxidation reaction is reported. Pd NPs with different coverage were deposited on Ni foams and three-dimensional graphene-Ni foams, respectively. Owning to the special three-dimensional structure of Ni foam, the Pd NPs-Ni foam composite exhibited remarkable activity and unusually long-term stability for methanol electro-oxidation. The introduced three-dimensional graphene prepared by conventional chemical vapour deposition improved the electrocatalytic performance. The results can be attributed to the Pd NPs with high electrochemical activity and unique properties for three-dimensional supports.