Synthesis of polyamides from p-Xylylene glycol and dinitriles

A series of polyamides were prepared by direct polyamidation of the p-Xylylene glycol with various commercially available dinitriles via Ritter reaction. All the synthesized polyamides showed good solubility in amide type solvents such as N-methyl-2-pyrrolidone, N,N-dimethyl acetamide, N,N-dimethyl formamide, and dimethyl sulfoxide. They exhibited inherent viscosities in the range of 0.25–0.62 dl/g. Elemental analysis, differential scanning calorimetry, thermogravimetric analysis, infrared and H¹-NMR spectroscopies were used to characterize the polymers. According to the DSC analysis, the glass transition temperatures of the polyamides were found to be 95–174°C. Thermogravimetric analysis indicated that the polymers show the 10% weight loss temperatures in the range of 250–350°C.     

中间层成分对GTD-111/IN-718高温合金在瞬时液相连接过程中显微组织和力学性能的影响

研究使用不同的中间层瞬时液相连接两种异种高温合金的适用性.在1100℃、不同时间下瞬时液相连接GTD-111/IN-718体系,研究BNi-2、BNi-3和BNi-9三种类型的中间层对该体系显微组织和力学性能的影响.采用场发射扫描电子显微镜和能量色散光谱技术,研究接头区域的成分变化和显微组织.结果表明,非热凝固区Ni3...     

Rapid Separating and Enrichment of 4,4′-Dimethylsterols of Vegetable Oils by Solid-Phase Extraction

Phytosterols are separated into three classes: 4-desmethylsterols, 4-monomethylsterols and 4,4′-dimethylsterols. 4,4′-Dimethylsterols are used to detect vegetable oil adulteration and some compounds from this class can have anti-inflammatory and anticancer properties. There are methods such as thin layer chromatography (TLC) and solid phase extraction (SPE) used to separate phytosterol classes from each other. However, in some cases, separation of all three classes is not required. In addition, TLC has some drawbacks such as low recovery and it is time consuming. An SPE method has previously been used, but it was necessary to use high volume of solvents with this method to avoid coelution of phytosterol classes. In this study, an SPE (silica, 1 g) method was developed to separate and enrich only 4,4′-dimethylsterols from unsaponifiables of vegetable oil samples using 25 mL n-hexane and diethyl ether (95:5, v:v). This method was applied to hazelnut and olive oils and results were compared with those of TLC and the previously developed SPE method. Recovery of 4,4′-dimethylsterols was two times higher with the new SPE method compared with the TLC method. The newly developed SPE method generally gave a similar recovery compared with the previously developed SPE method. Moreover, the SPE method developed in this study has the advantage of using a 3.5 times lower volume of solvent than previously developed SPE methods. Because the newly developed SPE method has a single step requiring a low volume of solvents, it is rapid and simple, and can easily be used to detect olive oil adulteration with hazelnut oil and to analyze and quantify effective nutritional compounds in the 4,4′-dimethylsterols class.     

Incorporating Nano-silica as a Binder to Improve Corrosion Resistance of High Alumina Refractory Castables

In this study, four types of castables as calcium-aluminate cement (CAC)-bonded and nano-silica (NS)-bonded castables based on tabular-alumina and bauxite aggregates were prepared to investigate the replacement of the calcium-aluminate cement by NS. All samples were allowed to dry at 110 °C then fired at 800 and 1200 °C. Bulk density and apparent porosity of samples were measured. The molten aluminum static corrosion test (cup test) results showed that NS-bonded specimens had higher resistance to corrosion compared to CAC-bonded samples (based on the measured average aluminum penetration depth into the refractory texture). However no penetration was observed in bauxite NS-bonded samples. The results were consistent with dynamic corrosion test in aluminum melt carried out at 800 °C for 100 h. The small pore size in NS-bonded castables was found to be the main cause for high corrosion resistance as micro-pores prevented the melt to penetrate into the refractory.     

Introducing a proper hydrogen liquefaction concept for using wasted heat of thermal power plants-case study: Parand gas power plant

A hydrogen liquefaction concept with an innovative configuration and a capacity of 4 kg·s^-1 (345.6 t·d^-1) is developed. The concept involves an ammonia absorption refrigeration system for the pre-cooling of hydrogen and MR streams from 25 ℃ to -30 ℃. The ammonia absorption refrigeration system is fed by exhaust gases of the Parand gas power plant that are normally dissipated to the environment with a temperature of 546 ℃. The simulation is performed by Aspen HYSYS V9.0, using two separate equations of state for simulating hydrogen and MR streams to gain more accurate results especially for ortho-para conversion. Results show that conversion enthalpy estimated by Aspen HYSYS, fits very well to the experimental data. Determining the important independent variables and composition of MRs are done using trial and error procedure, a functional and straightforward method for complicated systems. The minimum temperature limit in the cooling section is lowered, and an ortho-para converter is implemented in this section. The proposed concept performs well from energy aspects and leads to COP and SEC equal to 0.271 and 4.54 kW·h·kg^-1, respectively. The main advantage of this study is in the low SEC, eliminating the losses of the distribution network, and improving the ability of the hydrogen liquefaction for energy storage in off-peak times.     

Theoretical and experimental analyses of rheological,compatibility and mechanical properties of PVMQ/XNBR-g GMA/XNBR/GO ternary hybrid nanocomposites

Iranian Polymer Journal - Effects of graphene oxide (GO) on various properties of rubber hybrid nanocomposites based on PVMQ/XNBR-g-GMA/XNBR (phenyl-vinyl-methyl-polysiloxane/carboxylated nitrile...     

TP53 Targeted Deep Sequencing of Cell-Free DNA in Esophageal Squamous Cell Carcinoma Using Low-Quality Serum: Concordance with Tumor Mutation

Circulating cell-free DNA (cfDNA) is emerging as a potential tumor biomarker. CfDNA-based biomarkers may be applicable in tumors without an available non-invasive screening method among at-risk populations. Esophageal squamous cell carcinoma (ESCC) and residents of the Asian cancer belt are examples of those malignancies and populations. Previous epidemiological studies using cfDNA have pointed to the need for high volumes of good quality plasma (i.e., >1 mL plasma with 0 or 1 cycles of freeze-thaw) rather than archival serum, which is often the main available source of cfDNA in retrospective studies. Here, we have investigated the concordance of TP53 mutations in tumor tissue and cfDNA extracted from archival serum left-over from 42 cases and 39 matched controls (age, gender, residence) in a high-risk area of Northern Iran (Golestan). Deep sequencing of TP53 coding regions was complemented with a specialized variant caller (Needlestack). Overall, 23% to 31% of mutations were concordantly detected in tumor and serum cfDNA (based on two false discovery rate thresholds). Concordance was positively correlated with high cfDNA concentration, smoking history (p-value = 0.02) and mutations with a high potential of neoantigen formation (OR; 95%CI = 1.9 (1.11–3.29)), suggesting that tumor DNA release in the bloodstream might reflect the effects of immune and inflammatory context on tumor cell turnover. We identified TP53 mutations in five controls, one of whom was subsequently diagnosed with ESCC. Overall, the results showed that cfDNA mutations can be reliably identified by deep sequencing of archival serum, with a rate of success comparable to plasma. Nonetheless, 70% non-identifiable mutations among cancer patients and 12% mutation detection in controls are the main challenges in applying cfDNA to detect tumor-related variants when blindly targeting whole coding regions of the TP53 gene in ESCC.     

Mesoporous SBA-15@n-Pr-THAM-ZrO organic–inorganic hybrid: as a highly efficient reusable nanocatalyst for the synthesis of polyhydroquinolines and 2,3-dihydroquinazolin-4 (1h)-ones

Journal of Porous Materials - In this work, tris(hydroxymethyl)aminomethane-Zirconium complex supported on modified SBA-15 (SBA-15@n-Pr-THMAM-ZrO) prepared as a novel mesoporous catalyst. The...     

Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network

In this paper we have investigated the differences between the prices of different commercial oils of the Persian Gulf region. The prices of 7 different crude oils from Iran, Kuwait, Saudi Arabia, Oman, Abu Dhabi and Dubai were compared with the benchmark light oil of Saudi Arabia over the period January 2000-April 2010. A neural network is introduced to forecast the price of any commercial oil in these crude oils, provided that the price of the benchmark light oil of Saudi Arabia is already known or is predicted by another forecasting method. The designed neural network is able to predict the differences in the oil prices with an average error of 8.82% for testing and 7.24% for training data. It is claimed that the present method can promote the forecasting power of existing models to predict the price of any commercial oil instead of an average or benchmark value.     

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.