The Experts below are selected from a list of 33066 Experts worldwide ranked by ideXlab platform
Ralf Bundschuh - One of the best experts on this subject based on the ideXlab platform.
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a statistical analysis of rna folding algorithms through Thermodynamic Parameter perturbation
Nucleic Acids Research, 2005Co-Authors: D M Layton, Ralf BundschuhAbstract:Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured Parameters. Here, we study how sensitive structure prediction algorithms are to changes in these Parameters. We found already that for changes corresponding to the actual experimental error to which these Parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under Parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using Parameter perturbation is proposed, and its limitations are discussed.
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a statistical analysis of rna folding algorithms through Thermodynamic Parameter perturbation
arXiv: Quantitative Methods, 2004Co-Authors: D M Layton, Ralf BundschuhAbstract:Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured Parameters. Here, we study how sensitive structure prediction algorithms are to changes in these Parameters. We find that already for changes corresponding to the actual experimental error to which these Parameters have been determined 30% of the structure are falsly predicted and the ground state structure is preserved under Parameter perturbation in only 5% of all cases. We establish that base pairing probabilities calculated in a thermal ensemble are a viable though not perfect measure for the reliability of the prediction of individual structure elements. A new measure of stability using Parameter perturbation is proposed, and its limitations discussed.
Zeid A Alothman - One of the best experts on this subject based on the ideXlab platform.
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synthesis of chitosan composite iron nanoparticles for removal of diclofenac sodium drug residue in water
International Journal of Biological Macromolecules, 2020Co-Authors: Zeid A Alothman, Ahmad Yacine Badjah, Omar M L AlharbiAbstract:Abstract Iron composite nanoparticles were prepared (90% yield) using macromolecule chitosan and characterized by spectroscopic techniques (FT-IR, XRD, SEM, TEM & EDX). These were utilized to remove diclofenac sodium in water. The adjusted Parameters were 400 μg/ L, 50.0 min., 5.0, 2.0 g/ L and 25.0 °C as concentration, contact time, pH, adsorbent amount and temperature for the elimination of diclofenac sodium in water with maximum 85% elimination. The sorption was spontaneous with exothermic. Data followed Langmuir, Temkin and Dubinin-Radushkevich models. Thermodynamic Parameter ΔG° values were −12.19, −13.74 and −15.67 kJ/mol at 20, 25 and 30 °C temperatures. The values of ΔH° and ΔS° were 8.58 and 20.84 kJ/mol. Pseudo-first-order and liquid film diffusion mechanisms were proposed for the adsorption. This adsorption method is fast, effective eco-friendly and low-cost as it may be used in natural circumstances of water resources. The sorption method may be applied for the elimination of diclofenac sodium in any water body at a huge and financial scale.
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synthesis of composite iron nano adsorbent and removal of ibuprofen drug residue from water
Journal of Molecular Liquids, 2016Co-Authors: Zeid A Alothman, Abdulrahman AlwarthanAbstract:Abstract Ibuprofen drug residue is found in some water resources, which is toxic to human beings and flora and fauna. The removal of ibuprofen drug residue from water is carried out on composite iron nano adsorbent. The composite iron nano adsorbent was prepared by green technology and characterized by FT-IR, XRD, SEM, TEM and EDX techniques. The residual ibuprofen in water was analyzed by HPLC using new generation Sunshell C18 column (150 × 4.61 mm; 2.6 μm). The optimized batch experimental Parameters were 60 μg L− 1, 30.0 min., 7.0, 1.0 g L− 1, and 25.0 °C as concentration, contact time, pH, adsorbent amount and temperature. Adsorption data followed Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. Thermodynamic Parameter ΔG° values were − 10.23, − 10.24 and − 10.26 kJ/mol at 20, 25 and 30 °C temperatures. The values of ΔH° and ΔS° were − 2.71 and 24.20 × 10− 3. These values indicated favorable and exothermic sorption. Pseudo-first-order and liquid film diffusion mechanisms of the adsorption were proposed. Developed method was speedy, environment friendly and inexpensive due to its nature to be used under natural conditions of water resources with 92% removal of ibuprofen. Moreover, ease of desorption made this adsorbent recyclable. The developed method may be useful for the removal of ibuprofen drug residue from natural water resources at pHs 7.0 with low adsorbent amount and agitating time. The sorption method may be used for the removal of ibuprofen from any water body at large and economic scale.
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Green synthesis of functionalized iron nano particles and molecular liquid phase adsorption of ametryn from water
Journal of Molecular Liquids, 2016Co-Authors: Imran Ali, Zeid A Alothman, Abdulrahman Al-warthanAbstract:Abstract Second generation herbicide ametryn [2-(ethylamino)-4-isopropylamino-6-methyl-thio- s -triazine] is being used to control broadleaf weeds and annual grasses in sugarcane, bananas, maize and pineapple fields; contaminating water resources. The adsorption method of ametryn removal was presented using functionalized iron nano particles as adsorbent. This adsorbent was prepared by green method, functionalized with 1-butyl-3-methylimidazolidium bromide and characterized by FT-IR, SEM and XRD techniques. Residual ametryn in water was analyzed by optimized HPLC. The optimized batch experiment Parameters were 30.0 μg/L, 30.0 min, 7.0, 2.5 g/L and 20.0 °C as concentration, contact time, adsorbent amount and temperature. Adsorption data followed Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. Thermodynamic Parameter such as ΔG°, ΔH° and ΔS° were − 6.05, − 6.11 and − 6.15 kJ/mol at 20, 25 and 30 °C temperatures; − 6.68 kJ/mol and − 2.45 × 10 − 3 kJ/mol K. These values indicated sorption spontaneous and exothermic. Pseudo-second-order and liquid film diffusion mechanisms of the adsorption were proposed. Developed sorption method was speedy, environmental friendly and inexpensive due to its nature to be used under natural conditions of water resources. The method work at natural water resources pHs with low adsorbent amount and agitating time. The sorption method may be used for the removal of ametryn from any water body at large and economic scale.
R Karvembu - One of the best experts on this subject based on the ideXlab platform.
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adsorption and corrosion inhibition behaviour of n phenylcarbamothioyl benzamide on mild steel in acidic medium
Progress in Organic Coatings, 2012Co-Authors: Mayakrishnan Gopiraman, N Selvakumaran, Devarayan Kesavan, R KarvembuAbstract:Abstract Corrosion inhibition property of N-(phenylcarbamothioyl)benzamide (PCB) on mild steel in 1.0 M HCl solution has been investigated using chemical (weight loss method) and electrochemical techniques (potentiodynamic polarization and AC impedance spectroscopy). The inhibition efficiencies obtained from all the methods are in good agreement. The thiourea derivative is found to inhibit both anodic and cathodic corrosion as evaluated by electrochemical studies. The inhibitor is adsorbed on the mild steel surface according to Langmuir adsorption isotherm. The adsorption mechanism of inhibition was supported by spectroscopic (UV–visible, FT-IR, XPS), and surface analysis (SEM–EDS) and adsorption isotherms. The Thermodynamic Parameter values of free energy of adsorption (ΔGads) reveals that inhibitor was adsorbed on the mild steel surface via both physisorption and chemisorption mechanism.
Yong Yang - One of the best experts on this subject based on the ideXlab platform.
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design of high entropy alloys a single Parameter Thermodynamic rule
Scripta Materialia, 2015Co-Authors: Q Wang, C T Liu, Yong YangAbstract:Assuming random mixing of atoms, design of high entropy alloys (HEAs) was used to follow a simple route by maximizing their configurational entropy of mixing. Here we propose a single-Parameter design paradigm taking into account formation enthalpy and the excessive entropy of mixing, which arises from dense atomic packing and atomic size misfit. The proposed paradigm is verified using the data hitherto reported and proven to be a physically accepted Thermodynamic Parameter for the design of HEAs.
D M Layton - One of the best experts on this subject based on the ideXlab platform.
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a statistical analysis of rna folding algorithms through Thermodynamic Parameter perturbation
Nucleic Acids Research, 2005Co-Authors: D M Layton, Ralf BundschuhAbstract:Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured Parameters. Here, we study how sensitive structure prediction algorithms are to changes in these Parameters. We found already that for changes corresponding to the actual experimental error to which these Parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under Parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using Parameter perturbation is proposed, and its limitations are discussed.
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a statistical analysis of rna folding algorithms through Thermodynamic Parameter perturbation
arXiv: Quantitative Methods, 2004Co-Authors: D M Layton, Ralf BundschuhAbstract:Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured Parameters. Here, we study how sensitive structure prediction algorithms are to changes in these Parameters. We find that already for changes corresponding to the actual experimental error to which these Parameters have been determined 30% of the structure are falsly predicted and the ground state structure is preserved under Parameter perturbation in only 5% of all cases. We establish that base pairing probabilities calculated in a thermal ensemble are a viable though not perfect measure for the reliability of the prediction of individual structure elements. A new measure of stability using Parameter perturbation is proposed, and its limitations discussed.
