在三通道α-螺旋蛋白质中传递生物能量的孤子的特性

用庞小峰先生提出的生物能量传递的新理论和 Runge-Kutta数值模拟方法,研究了三通道α-螺旋蛋白质中激发的孤子的动力学特性,求出在0K和300K温度下该孤子稳定地沿蛋白质链传递.由此可见,它能担任在蛋白质分子中传递生物能量的功能.从而再次证实了新理论的可利用性.     

Post-harvest processing methods for reduction of silica and alkali metals in wheat straw

Silica and alkali metals in wheat straw limit its use for bioenergy and gasification. Slag deposits occur via the eutectic melting of SiO₂ with K₂O, trapping chlorides at surfaces and causing corrosion. A minimum melting point of 950°C is desirable, corresponding to an SiO₂:K₂O weight ratio of about 3:1. Mild chemical treatments were used to reduce Si, K, and Cl, while varying temperature, concentration, % solids, and time. Dilute acid was more effective at removing K and Cl, while dilute alkali was more effective for Si. Reduction of minerals in this manner may prove economical for increasing utilization of the straw for combustion or gasification.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

生物能量传递新模型中的孤立子特性研究

从庞提出的新的生物能量传递理论出发,用Runge-Kutta方法和蛋白质分子的特征参数值对该理论的基本动力学方程进行了数值模拟,求出了孤立子在自由运动下、在相互碰撞时以及它在长时间运动后的特征.研究表明,在这些运动过程中,这个孤立子始终保持稳定状态.于是看到它能真正承担起在蛋白质分子传递生物能量的功能.从而从数值模拟的角度证明了新理论的可利用性.     

Structural and nutritional characterization of macromolecular complexes in new bioenergy feedstock by infrared radiation with advanced molecular spectroscopy and spectral chemometrics

The objective of this research was to characterize new bioenergy feedstocks, in comparison with conventional feedstock, by infrared radiation with advanced molecular spectroscopy in terms of association of molecular structure with nutrient delivery in ruminant livestock systems. This study focused on the effect of molecular structure changes on nutrient delivery to ruminant systems. The updated methods of various lab, animal, and modeling techniques for nutrient delivery study in ruminant livestock systems were reviewed. The molecular spectral analyses used in this study included both univariate and multivariate molecular spectral analyses. The relationship between molecular structural features and truly absorbed nutrient supply was quantified. The research provides information on how inherent structure features in the new types of bioenergy feedstock on a molecular basis affected nutrient delivery in ruminant livestock systems.     

Technoeconomic analysis of the dilute sulfuric acid and enzymatic hydrolysis process for the conversion of corn stover to ethanol

Technoeconomic analysis has been used to guide the research and development of lignocellulosic biofuels production processes for over two decades. Such analysis has served to identify the key technical barriers for these conversion processes so that research can be targeted most effectively on the pertinent challenges. The tools and methodology used to develop conceptual conversion processes and analyze their economics are presented here. In addition, the current process design and economic results are described for dilute acid pretreatment followed by enzymatic hydrolysis and fermentation. Modeled ethanol costs of $1.33/gallon (in consistent year 2007 dollars) are being targeted for this commercial scale corn stover conversion process in 2012. State of technology models, which take actual research results and project them to commercial scale, estimate an ethanol cost of $2.43/gallon at present. In order to further reduce costs, process improvements must be made in several areas, including pretreatment, enzymatic hydrolysis, and fermentation. As the biomass industry develops, new fuels and new feedstocks are being researched. Technoeconomic analysis will play a key role in process development and targeting of technical and economic barriers for these new fuels and feedstocks.

纤维素类芒属草本能源植物品质近红外光谱快速检测技术研究

我国生物质能源产业近年来得到快速发展,但对能源草的研究还处于初级阶段,如果能建立全面的能源植物木质素、纤维素、半纤维素的近红外预测模型数据库,将有助于优良品种的筛选、能源植物能用性能的评价及生物质能源产业在线控制。本研究采用傅里叶变换近红外光谱(FT-NIR)技术结合偏最小二乘法(PLSR)建立了荻、南荻、奇岗、芒四种芒属能源植物品质指标(纤维素,半纤维素,木质素和灰分)近红外预测模型,并在此基础上研究了样本粒度对模型的影响。研究结果表明：(1)四种芒属能源植物茎秆中纤维素,半纤维素和木质素含量误差均方根(RMSECV)分别为1.35％(R2=0.88),0.39%(R2=0.91)和0.35%(R2=0.80),叶片中纤维素,半纤维素和木质素含量误差均方根(RMSECV)分别为0.72%(R2=0.88),0.85%(R2=0.85)和0.44 (R2=0.87),所建的纤维素,半纤维素和木质素的近红外校准模型在预测未知样品含量时效果较好,但灰分含量预测效果不理想;(2)2和0.5 mm粒度样品所建近红外模型均满足样品检测精度要求,但考虑到时间和人工成本,建议在工厂对能源植物原料品质进行分析时,采用2 mm样品建模。     

Advance in ATP-involved active self-assembled systems

Molecular assembly offers a promising strategy to construct active systems by using biomolecules as building blocks. Such assembled systems simulate or regulate important biological activities and show great promise in wide bioapplications. In this short review, we focus on the recent progress in ATP-involved active self-assembled systems. ATP-generated active systems are constructed with hierarchical structures via molecular assembly to produce ATP by using various external influences to generate proton gradient. Further, we highlight present active supermolecular systems driven by ATP as chemical fuel. Finally, we discuss the key challenges and perspectives in the future research.     

Enzyme-based biocatalysis for the treatment of organic pollutants and bioenergy production

Enzyme-based biocatalysis is emerging as an advanced technique to develop green processes that help to maintain the sustainability of the environment. The bioremediation of toxic organic pollutants and waste to bioenergy production using enzymes as biocatalysts is rapidly growing due to its eco-friendly and sustainable nature. Additionally, a range of microbial species that typically grow on organic wastes can be used to produce these enzymes in an efficient manner. This is seen as a potential strategy for the development of cost-effective manufacturing for a number of biotechnological applications. The present study discusses biocatalysis as a promising and sustainable method toward the bioremediation of hazardous organic pollutants as well as for bioenergy production, based on the immense potential of enzymes as biocatalysts. Emphasis has been placed on evaluating the critical elements that can enhance the production of enzymes used as biocatalysts, as well as their functional effectiveness and stability.     

An energy model for nanomechanical deflection of cantilever-DNA chip

Nanomechanical behavior of cantilever-DNA chip in label-free biodetection is investigated by the energy method. First, using an equation of state for DNA liquid crystals and an alternative two-variable model for laminated cantilever beams, a relationship between chip energy and some factors, such as nanogeometrical, physical, chemical characteristics of DNA molecules, microscopical geometric dimension, macroscopical mechanical properties of chip, etc., is formulated in consideration of electrostatic energy, hydration energy and configurational fluctuations of DNA layer as well as mechanical energy of chip. Second, theoretical predictions of nanomechanical deflection of DNA chip by the minimum principle of energy are compared with experimental data in Wu's experiments. Third, the influence of stochastic interchain distances and stochastic elastic modulus on chip deflection is investigated. The validity of the simplified two-layer-beam model is also studied. Numerical results show that chip deflection enhances with the increase in length of DNA chains, and the interchain distances should be carefully controlled no less than 4 nm during the process of probe molecules self-assembly.