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Nanoindentation has been widely utilized for measuring mechanical properties at small scales over the past three decades. Indentation size effect (ISE) is a crucial phenomenon in nanoindentation testing of crystalline materials, which is usually manifested as an increase of hardness with a decrease of indent size. Tremendous efforts have been made to understand ISE with the aim of eliminating its influence on determining the intrinsic mechanical properties. More importantly, ISE is a key phenomenon for exploring the unique deformation mechanisms of crystalline materials at the nanoscale. We critically review the experimental observations of ISE in crystalline materials, focusing on the influences of the indenter geometry and crystal structure. In addition, the mechanistic models proposed for ISE as well as recent findings by numerical simulations are also examined in detail to explore the origins of ISE and identify the unsolved challenges.

The Sigara lateralis (Leach, 1817) is a small aquatic insect belonging to the family Corixidae. The study aims to reveal the genomic data of the mitochondrial genome of S. lateralis. The length of its circular mitochondrial genome is 15,725 bp long with an A + T bias (75.96%). The mitogenome comprises 37 genes, including 13 protein-coding genes (PCGs), 22 transfer RNA genes, and two rRNA genes. The phylogenetic analyses showed that the S. lateralis is the closest to S. septemlineata. These findings will help the conservation of Corixidae from the perspective of genetic evolution.

Objective The millisecond-level precision of NTP (network time protocol) in train communication networks has increasingly failed to meet the requirements of high traffic and deterministic transmission. Meanwhile, existing research is limited to applying the PTP (precision time protocol) to train communication scenarios, lacking research on time synchronization in time-sensitive networks within train communication networks. Therefore, it is necessary to conduct relevant analysis. Method Firstly, based on the current research status of time synchronization in train communication networks, the deviation and delay in train communication networks are analyzed according to time-sensitive network synchronization protocols. Secondly, redundancy mechanis for clock service source failures and jumps are proposed. Finally, the effectiveness of the redundancy mechanism is verified through simulation of the time synchronization process. Result & Conclusion Time-sensitive networks are well suited for time synchronization of clock service sources in train communication networks, and their redundancy mechanisms effectively mitigate synchronization errors caused by clock service source failures and jumps.

Objective TCN (train communication networks) has complex topologies and requires high real-time transmission capabilities. Currently, train data transmission mainly relies on TRDP (train real-time data protocol) for carriage, while there are issues of network latency uncertainty. TSN is thus adopted to ensure bounded latency for data transmission, addressing the aforementioned issues faced by TCN. Method A method called TTAM (TRDP and TSN adaptive method) is proposed to achieve protocol conversion between TRDP and TSN, ensuring data transmission in the network. Result & Conclusion Through the establishment of a TSN testbed, the functionality and performance of TSN networks (IEEE 802.1 AS and IEEE 802.1 Qbv) are tested, validating the effectiveness of TTAM in TSN technology application.

This paper presents an innovative numerical modeling framework capable of generating highly realistic 3D mesoscale multi-phase concrete models with unprecedented efficiency and accuracy. Addressing a significant wide range in aggregate volume fraction (0 to 80%) and featuring rapid model generation capabilities, our framework marks a breakthrough in reducing computational time—achieving simulations of 1 million elements within mere 30 s on readily available hardware. By analyzing randomly generated concrete samples across varying compositions, our algorithm can provide deep mesoscopic insights into their compressive properties, validated against a wide array of experimental results. Additionally, our comprehensive analytical model sheds light on the intricate roles of aggregate volume fraction and porosity, enhancing understanding of the meso-scale compressive behavior. We also make the source code available on GitHub, offering a valuable tool for the engineering and research community to optimize concrete material design and performance.

Size effect of the nickel-based single crystal superalloy DD6 is investigated in this study by performing nanoindentation experiments and finite element (FE) simulations by emphasizing the strain rate effect. An analytical method is proposed to identify whether a material has the indentation size effect (ISE) based on incremental formulations of the indentation strain rate. By taking ISE as a critical factor, the contact area is further described by considering the curvature radius of non-ideal indenters due to wear issues in practice. In order to emphasize the strain rate effect during indentations, the material parameters dominating mechanical properties are adopted from the strain rate dependent Johnson–Cook model, particular for the nickel-based single crystal superalloy. Consequently, the dimensional ISE analysis is performed by proposing a theoretical framework to establish the relationship among the indentation strain rate, the indenter tip curvature radius and the applied load–penetration depth (P–h) curve for characterizing nanoindentation responses. By identifying the indenter curvature radii and loading time as the important external factors of hardness measurement, FE simulations are validated to achieve good agreement with experimental data. The validated FE predictions are reliably employed to calibrate the dimensionless model of hardness with different indenter curvature radii and loading rates. In the proposed form of the dimensional hardness model, it is found that the indenter curvature radius can be perfectly independent of the material constitutive properties and loading rates. It reveals the potential and advantageous determination of characteristic length of materials by using nanoindentation.

Introduction: Human topoisomerase 1 (TOP1) is an important target of various anticancer compounds. The design and discovery of inhibitors targeting TOP1 are of great significance for the development of anticancer drugs. Evodiamine and thieno [2,3-d] pyridine hybrids show potential antitumor activity. Herein, the anti-gastric cancer activities of these hybrids were investigated.Methods: The inhibitory effects of different concentrations of ten evodiamine derivatives on the gastric cancer cell line SGC-7901 were assessed using a methyl thiazolyl tetrazolium assay. Compounds EVO-1 and EVO-6 strongly inhibited gastric cancer cell proliferation, with inhibition rates of 81.17% ± 5.08% and 80.92% ± 2.75%, respectively. To discover the relationship between the structure and activity of these two derivatives, density functional theory was used to investigate their optimized geometries, natural population charges, frontier molecular orbitals, and molecular electrostatic potentials. To clarify their anti-gastric cancer mechanisms, molecular docking, molecular dynamics simulations, and binding free energy calculations were performed against TOP1.Results: The results demonstrated that these compounds could intercalate into the cleaved DNA-binding site to form a TOP1–DNA–ligand ternary complex, and the ligand remained secure at the cleaved DNA-binding site to form a stable ternary complex. As the binding free energy of compound EVO-1 with TOP1 (−38.33 kcal·mol−1) was lower than that of compound EVO-6 (−33.25 kcal·mol−1), compound EVO-1 could be a more potent anti-gastric cancer agent than compound EVO-6.Discussion: Thus, compound EVO-1 could be a promising anti-gastric cancer drug candidate. This study may facilitate the design and development of novel TOP1 inhibitors.

Preventing corrosion in the steel reinforcement of concrete structures is crucial for maintaining structural integrity and load-bearing capacity as it directly impacts the safety and lifespan of concrete structures. By preventing rebar corrosion, the durability and seismic performance of the structures can be significantly enhanced. This study investigates the hysteresis behavior of both corroded and non-corroded engineered cementitious composite (ECC)-glass-fiber-reinforced polymer (GFRP) spiral-confined reinforced-concrete (RC) columns. Employing experimental methods and finite element analysis, this research explores key seismic parameters such as crack patterns, failure modes, hysteretic responses, load-bearing capacities, ductility, stiffness degradation, and energy dissipation. The results demonstrate that ECC-GFRP spiral-confined RC columns, compared to traditional RC columns, show reduced corrosion rates, smaller crack widths, and fewer corrosion products, indicating superior crack control and corrosion resistance. Hysteresis tests revealed that ECC-GFRP columns, at a 20% target corrosion rate, exhibit an enhanced load-bearing capacity, ductility, and energy dissipation, suggesting improved durability and seismic resilience. Parametric and sensitivity analyses confirm the finite element model’s accuracy and highlight the significant influence of concrete compressive strength on load-bearing capacity. The findings suggest that ECC-GFRP spiral-confined RC columns offer promising applications in coastal and seismic-prone regions, enhancing corrosion resistance and mechanical properties, thus potentially reducing formwork costs and improving construction quality and efficiency.

The development of next-generation power electronics requires the semiconductor power devices with higher voltage endurance, higher electrical current density under high-temperature operations above 250 °C. Herein, we propose the uses of combined micron-sized Cu particles with Ag–amino composite to achieve a robust direct Cu–Cu bonding under a low sintering pressure and temperature (300 °C) in ambient atmosphere. The strong bonding strength of the Cu–Cu joint structure was evaluated as high as 45.3 MPa under 300 °C sintering temperature with low pressure (1 MPa). For the high temperature reliability evaluation, the sintered Cu–Ag composite Cu–Cu joints were stored at 250 °C in air. The shear strength of the joints remains above 30 MPa after 500 h ageing, suggesting an excellent mechanical stability during high temperature storage. Additionally, a SiC Schottky barrier diode with the sintered Cu–Ag composite joint structure was evaluated during a power cycle test at a junction temperature of 200 °C for 10,000 cycles. The thermal resistance of the joint structure was almost unaffected, and no large delamination was observed after the power cycle test, indicating that the Cu–Ag composite material exhibit remarkable potential in SiC power devices packaging.

Machine learning (ML) methods with good applicability to complex and highly nonlinear sequences have been attracting much attention in recent years for predictions of complicated mechanical properties of various materials. As one of the widely known ML methods, back-propagation (BP) neural networks with and without optimization by genetic algorithm (GA) are also established for comparisons of time cost and prediction error. With the aim to further increase the prediction accuracy and efficiency, this paper proposes a long short-term memory (LSTM) networks model to predict the dynamic compressive performance of concrete-like materials at high strain rates. Dynamic explicit analysis is performed in the finite element (FE) software ABAQUS to simulate various waveforms in the split Hopkinson pressure bar (SHPB) experiments by applying different stress waves in the incident bar. The FE simulation accuracy is validated against SHPB experimental results from the viewpoint of dynamic increase factor. In order to cover more extensive loading scenarios, 60 sets of FE simulations are conducted in this paper to generate three kinds of waveforms in the incident and transmission bars of SHPB experiments. By training the proposed three networks, the nonlinear mapping relations can be reasonably established between incident, reflect, and transmission waves. Statistical measures are used to quantify the network prediction accuracy, confirming that the predicted stress-strain curves of concrete-like materials at high strain rates by the proposed networks agree sufficiently with those by FE simulations. It is found that compared with BP network, the GA-BP network can effectively stabilize the network structure, indicating that the GA optimization improves the prediction accuracy of the SHPB dynamic responses by performing the crossover and mutation operations of weights and thresholds in the original BP network. By eliminating the long-time dependencies, the proposed LSTM network achieves better results than the BP and GA-BP networks, since smaller mean square error (MSE) and higher correlation coefficient are achieved. More importantly, the proposed LSTM algorithm, after the training process with a limited number of FE simulations, could replace the time-consuming and laborious FE pre- and post-processing and modelling.

In this study, α-Trimethylsilylmethyl-ω-dimethylsilyl-terminated polydimethylsiloxane, polydiethylsiloxane and poly[2,2,2-trifluoropropyl(methyl)siloxane] are synthesized using an anion catalyzed nonequilibrium polymerization reaction with trimethylsilylmethyl lithium as the initiator; hexamethylcyclotrisiloxane, hexaethylcyclotrisiloxane or 1,3,5-trimethyl-1,3,5-trifluoropropylcyclotrisiloxane as the monomer; and dimethylchlorosilane as an end-capping agent. Three kinds of α-trimethylsilylmethyl-ω-trimethoxylsilylethyl-terminated polysiloxanes are further prepared by hydrosilylation reaction of α-trimethylsilylmethyl-ω-dimethylsilyl-terminated polysiloxanes with vinyltrimethoxysilane using Karstedt’s catalyst. These α-trimethylsilylmethyl-ω-trimethoxylsilylethyl-terminated polysiloxanes are functionalized as in situ surface treatment agents for AlN particles. The effects of the structure of these polysiloxanes on the dispersion of AlN in the polysiloxane matrix and on the heat transfer performance of silicone pastes and silicone rubbers are investigated. A possible mechanism of surface treatment of AlN fillers by these novel silicone fluids is also discussed.

Background Periodontitis is a chronic multifactorial inflammatory disease. Porphyromonas gingivalis is a primary periopathogen in the initiation and development of periodontal disease. Evidence has shown that P. gingivalis is associated with systemic diseases, including IBD and fatty liver disease. Inflammatory response is a key feature of diseases related to this species.Methods C57BL/6 mice were administered either PBS, or P. gingivalis. After 9 weeks, the inflammatory response in gut, spleen, and liver was analyzed.Results The findings revealed significant disturbance of the intestinal microbiota and increased inflammatory factors in the gut of P. gingivalis-administered mice. Administrated P. gingivalis remarkably promoted the secretion of IRF-1 and activated the inflammatory pathway IFN-γ/STAT1 in the spleen. Histologically, mice treated with P. gingivalis exhibited hepatocyte damage and lipid deposition. The inflammatory factors IL-17a, IL-6, and ROR-γt were also upregulated in the liver of mice fed with P. gingivalis. Lee’s index, spleen index, and liver index were also increased.Conclusion These results suggest that administrated P. gingivalis evokes inflammation in gut, spleen, and liver, which might promote the progression of various systemic diseases.

Objective To study the changes of pituitary-related hormones in patients with prolonged disorders of consciousness (pDOC) and its influence on the prognosis of patient in order to explore the risk factor of hormone levels with consciousness level and prognosis of patients. Methods The clinical data of 87 pDOC patients admitted to the Department of Neurosurgery of Beijing Tiantan Hospital affiliated to Capital Medical University and the Department of Neurosurgery of the Seventh Medical Center of Chinese PLA General Hospital from January 2018 to December 2020 were analyzed retrospectively. According to the improvement of patients' consciousness, they were divided into consciousness recovery group (n=39) and consciousness non-recovery group (n=48). The secretion levels of pituitary related hormones were measured in the 2 groups, including free triiodothyronine (FT3), free thyroxine (FT4), thyroid stimulating hormone (TSH), adrenocorticotropic hormone (ACTH), growth hormone (GH) and cortisol and prolactin (PRL). According to Coma Recovery Scale-Revised (CSR-R) score, the patients were assigned into vegetative state VS group (n=61) and minimal consciousness state MCS group (n=26). The relationship between the changes of pituitary hormones and consciousness of pDOC patients was analyzed. According to the normal standard of hormone level, the patients were divided into normal hormone level group (n=25) and abnormal hormone level group (n=62). Univariate analysis and logistic regression model were used to explore the influencing factors (age, sex, cause of injury, course of disease, preoperative CRS-R score and injured location, etc.) of pDOC patients. Results Among the 87 pDOC patients, 62 (71.3%) had abnormal pituitary hormones, including 42 cases of single hormone abnormality (67.7%), and 20 cases of more than 2 changes (32.3%). The types and proportions of abnormal pituitary hormones were: GH 18 cases (29.0%), TSH and FT3/FT4 19 cases (30.6%), PRL 11 cases (17.7%), ACTH 8 cases (12.9%), and COR6 cases (9.7%). In the comparison of the differences of pituitary hormone levels between the VS group and MCS group, there were significant differences in GH, TSH, PRL and ACTH between the 2 groups (P < 0.01). Compared with the normal hormone level group, the abnormal hormone level group has an older onset age (P=0.001), a lower proportion of MCS patients (P=0.001), a lower CRS-R score (P < 0.001), a longer ICU stay (P < 0.001), and a higher proportion of paroxysmal sympathetic hyperexcitability syndrome (PSH). Multivariate analysis indicated that age (OR=1.316, P=0.006), CRS-R score (OR=0.200, P=0.037), ICU stay time (OR=3.991, P=0.040) and PSH symptoms (OR=11.874, P=0.040) were major influencing factors for abnormal hormone levels. Conclusion The pDOC patients have a high proportion of abnormal pituitary hormones, which is positively correlated with CRS-R score of patients' consciousness level. ICU stay time and PSH symptoms are risk factors of abnormal pituitary hormones.

The load-penetration depth (P–h) curves of different metallic coating materials can be determined by nanoindentation experiments, and it is a challenge to obtain stress–strain response and elastoplastic properties directly using P–h curves. These problems can be solved by means of finite element (FE) simulation along with reverse analyses and methods, which, however, typically occupy a lengthy time, in addition to the low generality of FE methodologies for different metallic materials. To eliminate the challenges that exist in conventional FE simulations, a long short-term memory (LSTM) neural network is proposed in this study and implemented to deep learn the time series of P–h curves, which is capable of mapping P–h curves to the corresponding stress–strain responses for elastoplastic materials. Prior to the operation of the neural network, 1000 sets of indentation data of metallic coating materials were generated using the FE method as the training and validating sets. Each dataset contains a set of P–h curves as well as the corresponding stress–strain curves, which are used as input data for the network and as training targets. The proposed LSTM neural networks, with various numbers of hidden layers and hidden units, are evaluated to determine the optimal hyperparameters by comparing their loss curves. Based on the analysis of the prediction results of the network, it is concluded that the relationship between the P–h curves of metallic coating materials and their stress–strain responses is well predicted, and this relationship basically coincides with the power-law equation. Furthermore, the deep learning method based on LSTM is advantageous to interpret the elastoplastic behaviors of coating materials from indentation measurement, making the predictions of stress–strain responses much more efficient than FE analysis. The established LSTM neural network exhibits the prediction accuracy up to 97%, which is proved to reliably satisfy the engineering requirements in practice.

IDH1 mutations occur in about 20–30% of gliomas and are a promising target for the treatment of cancer. In the present study, the performance of aIDH1R132H was verified via glide-docking-based virtual screening. On the basis of the two crystal structures (5TQH and 6B0Z) with the best discriminating ability to identify IDH1R132H inhibitors from a decoy set, a docking-based virtual screening strategy was employed for identifying new IDH1R132H inhibitors. In the end, 57 structurally diverse compounds were reserved and evaluated through experimental tests, and 10 of them showed substantial activity in targeting IDH1R132H (IC50 < 50 μM). Molecular docking technology showed that L806-0255, V015-1671, and AQ-714/41674992 could bind to the binding pocket composed of hydrophobic residues. These findings indicate that L806-0255, V015-1671, and AQ-714/41674992 have the potential as lead compounds for the treatment of IDH1-mutated gliomas through further optimization.

The inherent defects of recycled aggregate concrete (RAC) include the complex interfacial transition zone (ITZ) and the many micro-cracks that appear during its producing process, which result in some inferior mechanical properties compared with natural aggregate concrete (NAC). This drawback usually prevents RAC from being selected for structural purposes. Existing research has shown that the strength and ductility of damaged concrete in compression members can be significantly enhanced through external confinement using fiber-reinforced polymer (FRP) wraps. This application has been widely used in concrete structural repair and retrofitting technology. However, research on the effects of RAC damage coupled with different load damage conditions is rare, as is information on the mechanical properties of RAC reinforced with FRP jackets. This paper presents the results of an experimental study on the behavior of pre-damaged recycled aggregate concrete cylinders that were repaired with carbon fiber-reinforced polymer (CFRP) or large rupture strain (LRS)-FRP jackets. Tests were conducted on 58 concrete cylinders with variations in the replacement ratio, damage levels, and FRP properties. Test results demonstrated that the ultimate strain and strength of damaged recycled aggregate concrete could be significantly enhanced by FRP jackets and that aggregate quality plays a vital role in the strength of confined concrete. Also, the energy absorption of CFRP- and LRS-FRP-confined RAC were evaluated. The analysis indicated that, compared with CFRP-confined RAC, LRS-FRP can greatly improve the energy absorption capacity of RAC; thus, LRS-FRP confined concrete has a good potential to achieve a ductile design for concrete columns, especially when used in seismic reinforcement.

The Solar Activity Magnetic Monitor (SAMM) Network (SAMNet) is a future UK-led international network of ground-based solar telescope stations. SAMNet, at its full capacity, will continuously monitor the Sun’s intensity, magnetic, and Doppler velocity fields at multiple heights in the solar atmosphere (from photosphere to upper chromosphere). Each SAMM sentinel will be equipped with a cluster of identical telescopes each with a different magneto-optical filter (MOFs) to take observations in K I, Na D, and Ca I spectral bands. A subset of SAMM stations will have white-light coronagraphs and emission line coronal spectropolarimeters. The objectives of SAMNet are to provide observational data for space weather research and forecast. The goal is to achieve an operationally sufficient lead time of e.g., flare warning of 2–8 h and provide many sought-after continuous synoptic maps (e.g., LoS magnetic and velocity fields, intensity) of the lower solar atmosphere with a spatial resolution limited only by seeing or diffraction limit, and with a cadence of 10 min. The individual SAMM sentinels will be connected to their master HQ hub where data received from all the slave stations will be automatically processed and flare warning issued up to 26 h in advance.

With the fast bloom of flexible electronics and green vehicles, it is vitally important to rationally design and facilely construct customized functional materials with excellent mechanical properties as well as high electrochemical performance. Herein, by utilizing two modern industrial techniques, digital light processing (DLP) and chemical vapor deposition (CVD), a unique 3D hollow graphite foam (HGF) is demonstrated, which shows a periodic porous structure and robust mechanical properties. Finite element analysis (FEA) results confirm that the properly designed gyroidal porous structure provides a uniform stress area and mitigates potential structural failure caused by stress concentrations. A typical HGF can show a high Young’s modulus of 3.18 MPa at a low density of 48.2 mg cm-3. The porous HGF is further covered by active MnO2 material with a high mass loading of 28.2 mg cm-2 (141 mg cm-3), and the MnO2/HGF electrode still achieves a satisfactory specific capacitance of 260 F g-1, corresponding to a high areal capacitance of 7.35 F cm-2 and a high volumetric capacitance of 36.75 F cm-3. Furthermore, the assembled quasi-solid-state asymmetric supercapacitor also shows remarkable mechanical properties as well as electrochemical performance.

Knowledge of academic words, academic formulas and general formulas is essential for second language learners, but resources for incidental learning of these lexical items are very limited, especially in English as a Foreign Language contexts. This study employed a corpus-driven approach to examine the potential of online news for incidental learning of core academic words, academic formulas, and general formulas. Twenty-six corpora were created from voice of America (VOA) news to represent the amounts of reading that learners with the reading speed of 200 words per minute (wpm) and those with the reading speed of 150 wpm could complete within certain periods of time if they read VOA news regularly for 40 minutes per day and 5 days per week. Then, occurrences of core academic words, academic formulas, and general formulas represented by well-known lists in each corpus were counted with corpus-based tools. Analysis showed that the number of core academic words, academic formulas, and general formulas increased steadily as more input was added. Depending on the kind of target vocabulary and the reading speed, reading VOA news regularly for 3 months to 3.5 years would likely offer learners opportunities to encounter nearly all target words/formulas and encounter at least 80% of them 12 or more times in their reading. This finding suggests that online news is a useful resource for incidental vocabulary learning. It also indicates the minimal amounts of reading needed for incidental learning of core academic words, academic formulas, and general formulas.

Lightning striking on power system may lead to severe accident and huge losses, which is the reason why line arresters are needed. During operation, the electrical field concentration in line arrester may lead to ablation and accelerated aging of insulation materials, and furthermore, cause mal-operation. A type of line arresters with internal series air gap (IGLA) is chosen by this study, which is distinguished from the previously used metallic oxide arrester, with better characteristic on anti-flashover when icing, but with worse characteristic on anti-flashover when polluted. This paper aims at studying the probable variation of power-frequency electric field of this new kind of IGLA under polluted and icing situation, and making further analysis on the reason why the electric field strength change like this.

As the distribution network environment becomes more complex, improving the optimization effect and running time is the key to distribution network reconfiguration. In this paper, firstly, the minimum branch power loss is used as the evaluation index of the result, and the hybrid algorithm of PSO algorithm and CS algorithm is selected as the optimization method of the reconstruction problem. Secondly, the mathematical model of the distribution network introduced by DG is simplified, and the static distribution network weight is used. It is a simplified way of power flow solving by introducing DG into the distribution network model. Based on the CS-PSO algorithm, the optimization calculation of the distribution network reconfiguration with DG is carried out. Finally, a calculation example proves the rationality and effectiveness of the method.

The mechanical behaviour and service life of the 63Sn-37Pb solder joint under coupled thermal-electrical loadings have been investigated. The mechanical properties of solder material are calibrated by uniaxial tensile experiments at different strain rates and working temperatures. In order to describe the mechanical responses of solder alloy, a constitutive model is proposed based on a thermo-visco-plastic flow rule. Furthermore, the fatigue life of solder joints is predicted by establishing a 3D finite element model under the coupled electrical-thermal-mechanical loadings. Temperature distribution, current density and thermal stress are numerically obtained in the solder joint. Based on the distribution of stress and plastic strain, the vulnerable location in the solder joint is determined, which is critical for predicting the service life based on the Coffin-Manson model. By varying the loading conditions, a parametric study is performed to develop an empirical formula of fatigue life, and the dominant factor under complicated working conditions is assessed. It is found that the current density and heat sink temperature pronouncedly influence the fatigue life of solder joint. The empirical predictions show reasonable accuracy compared with the Coffin-Manson model and finite element simulations.

The effect of shale ash (SA)-based catalysts (SA as carriers to support several transition metal salts, such as ZnCl2, NiCl2·6H2O, and CuCl2·2H2O) on oil shale (OS) pyrolysis was studied. Results showed that SA promoted OS pyrolysis, and the optimum weight ratio of OS:SA was found to be 2:1. The SA-supported transition metal salt catalyst promoted the OS pyrolysis, and the catalytic effect increased with increasing load of the transition metal salt within 0.1−3.0 wt%. The transition metal salts loaded on the SA not only promoted OS pyrolysis and reduced the activation energy required but also changed the yield of pyrolysis products (reduced shale oil and semi-coke yields and increased gas and loss yield). SA-supported 3 wt% CuCl2·2H2O catalyst not only exhibited the highest ability to reduce the activation energy in OS pyrolysis (32.84 kJ/mol) but also improved the gas and loss yield, which was 4.4% higher than the uncatalyzed reaction. The supporting transition metal salts on the SA also increased the content of short-chain hydrocarbons in aliphatic hydrocarbons in shale oil and catalyzed the aromatization of aliphatic hydrocarbons to form aromatic hydrocarbons. The catalytic activity of the transition metal salt on the SA-based catalyst for OS pyrolysis decreased in the order of CuCl2·2H2O > NiCl2·6H2O > ZnCl2.