Heat capacities of ionic liquids as a function of temperature at 0.1 MPa. measurement and prediction

Heat capacities of nine ionic liquids were measured from (293 to 358) K by using a heat flux differential scanning calorimeter. The impact of impurities (water and chloride content) in the ionic liquid was analyzed to estimate the overall uncertainty. The Joback method for predicting ideal gas heat capacities has been extended to ionic liquids by the generation of contribution parameters for three new groups. The principle of corresponding states has been employed to enable the subsequent calculation of liquid heat capacities for ionic liquids, based on critical properties predicted using the modified Lydersen-Joback-Reid method, as a function of the temperature from (256 to 470) K. A relative absolute deviation of 2.9% was observed when testing the model against 961 data points from 53 different ionic liquids reported previously and measured within this study.

Evaluation of Gas Solubility Prediction in Ionic Liquids using COSMOthermX

As the range of available ionic liquids increases, methods by which important engineering parameters such as gas solubilities can be estimated from simple structural information become ever more desirable. COSMO-based thermodynamic models, such as that used by COSMOthermX, allow the determination of such data for pure and mixed component systems. Herein, we evaluate the predictive capability of COSMOthermX through a comparison with literature data obtained from the IUPAC database which contains data for 15 gases in 27 ionic liquids, To determine any effect inherent to ionic liquids, gas solubility predictions were first performed for selected molecular solvents at constant temperature and pressure. Further estimations of gas solubility at temperatures ranging from (278 to 368) K at 0.1 MPa in water were performed for 14 gases. The Study has demonstrated that COSMOthermX is capable of predicting, qualitatively, gas solubilities in ionic liquids and, hence, reducing the amount of unnecessary experimental measurements prior to specific applications using ionic liquids.

Prediction of bisolute dye adsorption isotherms on activated carbon

The removal of acid dyes, Tectilon Blue 4R, Tectilon Red 2B and Tectilon Orange 3G, from single solute, bisolute and trisolute solutions by adsorption on activated carbon (GAC F400) has been investigated in isotherm experiments. Results from these experiments were modelled using the Langmuir and Freundlich adsorption isotherm theories with the Langmuir model proving to be the more suitable. The Ideal Adsorbed Solution (IAS) model was coupled with the Langmuir isotherm to predict binary adsorption on the dyes. The application of the IAS theory accurately simulated the experimental data with an average deviation of approximately 3% between modelled and experimental data.

Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach

Self-consolidating concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work conditions and also reduce the impact on the environment by elimination of the need for compaction. This investigation aimed at exploring the potential use of the neurofuzzy (NF) approach to model the fresh and hardened properties of SCC containing pulverised fuel ash (PFA) as based on experimental data investigated in this paper. Twenty six mixes were made with water-to-binder ratio ranging from 0.38 to 0.72, cement content ranging from 183 to 317 kg/m3 , dosage of PFA ranging from 29 to 261 kg/m3 , and percentage of superplasticizer, by mass of powder, ranging from 0 to 1%. Nine properties of SCC mixes modeled by NF were the slump flow, JRing combined to the Orimet, JRing combined to cone, V-funnel, L-box blocking ratio, segregation ratio, and the compressive strength at 7, 28, and 90 days. These properties characterized the filling ability, the passing ability, the segregation resistance of fresh SCC, and the compressive strength. NF model is constructed by training and testing data using the experimental results obtained in this study. The results of NF models were compared with experimental results and were found to be quite accurate. The proposed NF models offers useful modeling approach of the fresh and hardened properties of SCC containing PFA.

Prediction of the formation and stabilities of energetic salts and ionic liquids based on ab initio electronic structure calculations

A computational approach to predict the thermodynamics for forming a variety of imidazolium-based salts and ionic liquids from typical starting materials is described. The gas-phase proton and methyl cation acidities of several protonating and methylating agents, as well as the proton and methyl cation affinities of many important methyl-, nitro-, and cyano- substituted imidazoles, have been calculated reliably by using the computationally feasible DFT (B3LYP) and MP2 (extrapolated to the complete basis set limit) methods. These accurately calculated proton and methyl cation affinities of neutrals and anions are used in conjunction with an empirical approach based on molecular volumes to estimate the lattice enthalpies and entropies of ionic liquids, organic solids, and organic liquids. These quantities were used to construct a thermodynamic cycle for salt formation to reliably predict the ability to synthesize a variety of salts including ones with potentially high energetic densities. An adjustment of the gas phase thermodynamic cycle to account for solid- and liquid-phase chemistries provides the best overall assessment of salt formation and stability. This has been applied to imidazoles (the cation to be formed) with alkyl, nitro, and cyano substituents. The proton and methyl cation donors studied were as follows: HCl, HBr, HI, (HO)(2)SO2, HSO3CF3 (TfOH), and HSO3(C6H4)CH3 (TsOH); CH3Cl, CH3Br, CH3I, (CH3O)(2)SO2, CH3SO3CF3 (TfOCH3) and CH3SO3(C6H4)CH3 (TsOCH3). As substitution of the cation with electron-withdrawing groups increases, the triflate reagents appear to be the best overall choice as protonating and methylating agents. Even stronger alkylating agents should be considered to enhance the chances of synthetic success. When using the enthalpies of reaction for the gas-phase reactants (eq 6) to form a salt, a cutoff value of - 13 kcal mol(-1) or lower (more negative) should be used as the minimum value for predicting whether a salt can be synthesized.

Using time-to-contact information to assess potential collision modulates both visual and temporal prediction networks

Accurate estimates of the time-to-contact (TTC) of approaching objects are crucial for survival. We used an ecologically valid driving simulation to compare and contrast the neural substrates of egocentric (head-on approach) and allocentric (lateral approach) TTC tasks in a fully factorial, event-related fMRI design. Compared to colour control tasks, both egocentric and allocentric TTC tasks activated left ventral premotor cortex/frontal operculum and inferior parietal cortex, the same areas that have previously been implicated in temporal attentional orienting. Despite differences in visual and cognitive demands, both TTC and temporal orienting paradigms encourage the use of temporally predictive information to guide behaviour, suggesting these areas may form a core network for temporal prediction. We also demonstrated that the temporal derivative of the perceptual index tau (tau-dot) held predictive value for making collision judgements and varied inversely with activity in primary visual cortex (V1). Specifically, V1 activity increased with the increasing likelihood of reporting a collision, suggesting top-down attentional modulation of early visual processing areas as a function of subjective collision. Finally, egocentric viewpoints provoked a response bias for reporting collisions, rather than no-collisions, reflecting increased caution for head-on approaches. Associated increases in SMA activity suggest motor preparation mechanisms were engaged, despite the perceptual nature of the task.

Prediction of microRNAs affecting mRNA expression during retinal development.

Background: MicroRNAs (miRNAs) are small RNA molecules (similar to 22 nucleotides) which have been shown to play an important role both in development and in maintenance of adult tissue. Conditional inactivation of miRNAs in the eye causes loss of visual function and progressive retinal degeneration. In addition to inhibiting translation, miRNAs can mediate degradation of targeted mRNAs. We have previously shown that candidate miRNAs affecting transcript levels in a tissue can be deduced from mRNA microarray expression profiles. The purpose of this study was to predict miRNAs which affect mRNA levels in developing and adult retinal tissue and to confirm their expression.

Results: Microarray expression data from ciliary epithelial retinal stem cells (CE-RSCs), developing and adult mouse retina were generated or downloaded from public repositories. Analysis of gene expression profiles detected the effects of multiple miRNAs in CE-RSCs and retina. The expression of 20 selected miRNAs was confirmed by RT-PCR and the cellular distribution of representative candidates analyzed by in situ hybridization. The expression levels of miRNAs correlated with the significance of their predicted effects upon mRNA expression. Highly expressed miRNAs included miR-124, miR-125a, miR-125b, miR-204 and miR-9. Over-expression of three miRNAs with significant predicted effects upon global mRNA levels resulted in a decrease in mRNA expression of five out of six individual predicted target genes assayed.

Conclusions: This study has detected the effect of miRNAs upon mRNA expression in immature and adult retinal tissue and cells. The validity of these observations is supported by the experimental confirmation of candidate miRNA expression and the regulation of predicted target genes following miRNA over-expression. Identified miRNAs are likely to be important in retinal development and function. Misregulation of these miRNAs might contribute to retinal degeneration and disease. Conversely, manipulation of their expression could potentially be used as a therapeutic tool in the future.

Resting energy expenditure in non-ventilated, non-sedated patients recovering from serious traumatic brain injury: Comparison of prediction equations with indirect calorimetry values

Background & aims: Little is known about energy requirements in brain injured (TBI) patients, despite evidence suggesting adequate nutritional support can improve clinical outcomes. The study aim was to compare predicted energy requirements with measured resting energy expenditure (REE) values, in patients recovering from TBI.

Methods: Indirect calorimetry (IC) was used to measure REE in 45 patients with TBI. Predicted energy requirements were determined using FAO/WHO/UNU and Harris–Benedict (HB) equations. Bland– Altman and regression analysis were used for analysis.

Results: One-hundred and sixty-seven successful measurements were recorded in patients with TBI. At an individual level, both equations predicted REE poorly. The mean of the differences of standardised areas of measured REE and FAO/WHO/UNU was near zero (9 kcal) but the variation in both directions was substantial (range 591 to þ573 kcal). Similarly, the differences of areas of measured REE and HB demonstrated a mean of 1.9 kcal and range 568 to þ571 kcal. Glasgow coma score, patient status, weight and body temperature were signi?cant predictors of measured REE (p < 0.001; R²⁼ 0.47).

Conclusions: Clinical equations are poor predictors of measured REE in patients with TBI. The variability in REE is substantial. Clinicians should be aware of the limitations of prediction equations when estimating energy requirements in TBI patients.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.