Surface Tension of Liquid Organic Acids: An Artificial Neural Network Model.
Ontology highlight
ABSTRACT:
SUBMITTER: Pierantozzi M
PROVIDER: S-EPMC7998689 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:An aluminium-based metal-organic framework ((MOF), MIL-53(Al)), was hydrothermally synthesized, characterized and applied for the remediation of the herbicides dicamba (3,6-dichloro-2-methoxy benzoic acid) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in aqueous medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to design, optimize and predict the non-linear relationships between the independent and dependent variables. The shared interaction of the effects of key response parameters on the adsorption capacity were assessed using the central composite design-RSM and ANN optimization models. The optimum adsorption capacities for dicamba and MCPA are 228.5 and 231.9 mg g-1, respectively. The RSM ANOVA results showed significant p-values, with coefficients of determination (R 2) = 0.988 and 0.987 and R 2 adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave R 2 = 0.999 and 0.999, R 2 adjusted = 0.997 and 0.995 and root mean square errors (RMSEs) of 0.001 and 0.004 for dicamba and MCPA, respectively. In each set of experimental conditions used for the study, the ANN gave better prediction than the RSM, with high accuracy and minimal error. The rapid removal (∼25 min), reusability (5 times) and good agreement between the experimental findings and simulation results suggest the great potential of MIL-53(Al) for the remediation of dicamba and MCPA from water matrices.
| S-EPMC9058251 | biostudies-literature
Project description:Surface tension, an important property of liquids, is easily measured for bulk samples. However, for droplets smaller than one micron in size, there are currently no reported measurements. In this study, atomic force microscopy (AFM) and force spectroscopy have been utilized to measure surface tension of individual submicron sized droplets at ambient pressure and controlled relative humidity (RH). Since the surface tension of atmospheric aerosols is a key factor in understanding aerosol climate effects, three atmospherically relevant systems (NaCl, malonic and glutaric acids) were studied. Single particle AFM measurements were successfully implemented in measuring the surface tension of deliquesced particles on the order of 200 to 500 nm in diameter. Deliquesced particles continuously uptake water at high RH, which changes the concentration and surface tension of the droplets. Therefore, surface tension as a function of RH was measured. AFM based surface tension measurements are close to predicted values based on bulk measurements and activities of these three chemical systems. Non-ideal behaviour in concentrated organic acid droplets is thought to be important and the reason for differences observed between bulk solution predictions and AFM data. Consequently, these measurements are crucial in order to improve atmospheric climate models as direct measurements hitherto have been previously inaccessible due to instrument limitations.
| S-EPMC5490419 | biostudies-literature
Project description:Living organisms enhance their survival rate by excreting fluids at their surface, but man-made materials can also benefit from liquid secretion from a solid surface. Known approaches to secrete a liquid from solids are limited to passive release driven by diffusion, surface tension, or pressure. Remotely triggered release would give active control over surface properties but is still exceptional. Here, we report on an artificial skin that secretes functional fluids by means of radiofrequency electrical signals driven by dielectric liquid transport in a (sub-)microporous smectic liquid crystal network. The smectic order of the polymer network and its director determine the flow direction and enhance fluid transport toward the surface at pre-set positions. The released fluid can be reabsorbed by the skin using capillary filling. The fluid-active skins open avenues for robotic handling of chemicals and medicines, controlling tribology and fluid-supported surface cleaning.
| S-EPMC7487776 | biostudies-literature
Project description:Background & aimsHepatitis B surface antigen (HBsAg) seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB). This study aimed to develop artificial neural networks (ANNs) that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables.MethodsData from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion), and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs) were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC).ResultsSerum quantitative HBsAg (qHBsAg) and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001) and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P?=?0.197) and LRM-HBsAg seroconversion was solely based on qHBsAg (P?=?0.01). For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05). Although the performance of ANN-HBsAg seroconversion (AUROC 0.757) was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA.ConclusionsANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.
| S-EPMC4051672 | biostudies-literature
Project description:The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 - 12), catalyst concentration (0.7 - 1.7 wt/wt%), reaction temperature (48 - 62°C) and reaction time (50 - 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.
| S-EPMC7251367 | biostudies-literature
Project description:BACKGROUND:Successful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs. METHODS:Data from 1/12/2009 through 31/12/2011 of 3602 patients with planned extubation in Chi-Mei Medical Center's ICUs was used to train and test an artificial neural network (ANN). The input was 37 clinical risk factors, and the output was a failed extubation prediction. RESULTS:One hundred eighty-five patients (5.1%) had a failed extubation. Multivariate analyses revealed that failure was positively associated with therapeutic intervention scoring system (TISS) scores (odds ratio [OR]: 1.814; 95% Confidence Interval [CI]: 1.283?2.563), chronic hemodialysis (OR: 12.264; 95% CI: 8.556?17.580), rapid shallow breathing (RSI) (OR: 2.003; 95% CI: 1.378?2.910), and pre-extubation heart rate (OR: 1.705; 95% CI: 1.173?2.480), but negatively associated with pre-extubation PaO?/FiO? (OR: 0.529; 95%: 0.370?0.750) and maximum expiratory pressure (MEP) (OR: 0.610; 95% CI: 0.413?0.899). A multilayer perceptron ANN model with 19 neurons in a hidden layer was developed. The overall performance of this model was F?: 0.867, precision: 0.939, and recall: 0.822. The area under the receiver operating characteristic curve (AUC) was 0.85, which is better than any one of the following predictors: TISS: 0.58 (95% CI: 0.54?0.62; p < 0.001); 0.58 (95% CI: 0.53?0.62; p < 0.001); and RSI: 0.54 (95% CI: 0.49?0.58; p = 0.097). CONCLUSIONS:The ANN performed well when predicting failed extubation, and it will help predict successful planned extubation.
| S-EPMC6162865 | biostudies-other
Project description:Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors. Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed. Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales. Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage.
| S-EPMC6028566 | biostudies-literature
Project description:Super-hydrophobic, super-oleo(amphi)phobic, and super-omniphobic materials are universally important in the fields of science and engineering. Despite rapid advancements, gaps of understanding still exist between each distinctive wetting state. The transition of super-hydrophobicity to super-(oleo-, amphi-, and omni-)phobicity typically requires the use of re-entrant features. Today, re-entrant geometry induced super-(amphi- and omni-)phobicity is well-supported by both experiments and theory. However, owing to geometrical complexities, the concept of re-entrant geometry forms a dogma that limits the industrial progress of these unique states of wettability. Moreover, a key fundamental question remains unanswered: are extreme surface chemistry enhancements able to influence super-liquid repellency? Here, this was rigorously tested via an alternative pathway that does not require explicit designer re-entrant features. Highly controllable and tunable vertical network polymerization and functionalization were used to achieve fluoroalkyl densification on nanoparticles. For the first time, relative fluoro-functionalization densities are quantitatively tuned and correlated to super-liquid repellency performance. Step-wise tunable super-amphiphobic nanoparticle films with a Cassie-Baxter state (contact angle of >150° and sliding angle of <10°) against various liquids is demonstrated. This was tested down to very low surface tension liquids to a minimum of ca. 23.8 mN/m. Such findings could eventually lead to the future development of super-(amphi)omniphobic materials that transcend the sole use of re-entrant geometry.
| S-EPMC6728126 | biostudies-literature
Project description:The possible existence of a metastable liquid-liquid transition (LLT) and a corresponding liquid-liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal-multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP's location. These combined results-obtained from a purely first-principles approach with no empirical parameters-are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.
| S-EPMC7584908 | biostudies-literature
Project description:Unidirectional liquid spreading without energy input is of significant interest for the broad applications in diverse fields such as water harvesting, drop transfer, oil-water separation and microfluidic devices. However, the controllability of liquid motion and the simplification of manufacturing process remain challenges. Inspired by the peristome of Nepenthes alata, a surface-tension-confined (STC) channel with biomimetic microcavities was fabricated facilely through UV exposure photolithography and partial plasma treatment. Perfect asymmetric liquid spreading was achieved by combination of microcavities and hydrophobic boundary, and the stability of pinning effect was demonstrated. The influences of structural features of microcavities on both liquid spreading and liquid pinning were investigated and the underlying mechanism was revealed. We also demonstrated the spontaneous unidirectional transport of liquid in 3D space and on tilting slope. In addition, through changing pits arrangement and wettability pattern, complex liquid motion paths and microreactors were realized. This work will open a new way for liquid manipulation and lab-on-chip applications.
| S-EPMC7692703 | biostudies-literature