Computational modelling of effects of surface coverage on adsorption of benzene on Pt(111) surface

2021 ◽  
Author(s):  
P. K. Ayishabi ◽  
Raghu Chatanathodi
Keyword(s):  
Surface Coverage ◽  
Computational Modelling

scholarly journals Direct immunoassays and their performance – theoretical modelling of the effects of antibody orientation and associated kinetics

2018 ◽  
Vol 10 (10) ◽  
pp. 598-604 ◽  
Author(s):  
Dana Mackey ◽  
Eilís Kelly ◽  
Robert Nooney ◽  
Richard O'Kennedy
Keyword(s):  
Kinetic Parameters ◽  
Surface Coverage ◽  
Computational Modelling ◽  
Theoretical Modelling ◽  
Antibody Orientation ◽  
Optimal Surface

Mathematical and computational modelling are used to quantify immunoassay signals for various immobilized antibody orientations, antigen sizes and kinetic parameters, in order to determine optimal surface coverage.

The microstructure of functionalized poLyacrylonitrile beads for bioseparations

1990 ◽  
Vol 48 (4) ◽  
pp. 1094-1095
Author(s):  
Eduardo A. Kamenetzky ◽  
David A. Ley
Keyword(s):  
Aqueous Solution ◽  
Affinity Chromatography ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Surface Coverage ◽  
Vacuum Impregnation ◽  
Thin Sections ◽  
Selective Staining ◽  
Low Viscosity ◽  
Diamond Knife

The microstructure of polyacrylonitrile (PAN) beads for affinity chromatography bioseparations was studied by TEM of stained ultramicrotomed thin-sections. Microstructural aspects such as overall pore size distribution, the distribution of pores within the beads, and surface coverage of functionalized beads affect performance properties. Stereological methods are used to quantify the internal structure of these chromatographic supports. Details of the process for making the PAN beads are given elsewhere. TEM specimens were obtained by vacuum impregnation with a low-viscosity epoxy and sectioning with a diamond knife. The beads can be observed unstained. However, different surface functionalities can be made evident by selective staining. Amide surface coverage was studied by staining in vapor of a 0.5.% RuO4 aqueous solution for 1 h. RuO4 does not stain PAN but stains, amongst many others, polymers containing an amide moiety.

Further Evidence that Glycoprotein IIb-IIIa Mediates Platelet Spreading on Subendothelium

1991 ◽  
Vol 65 (02) ◽  
pp. 202-205 ◽  
Author(s):  
Harvey J Weiss ◽  
Vincet T Turitto ◽  
Hans R Baumgartner
Keyword(s):  
Platelet Adhesion ◽  
Surface Coverage ◽  
Rabbit Aorta ◽  
Conclusive Evidence ◽  
Initial Contact ◽  
Single Exposure ◽  
Vessel Segment ◽  
Multiple Exposures ◽  
Wall Interaction ◽  
Platelet Spreading

SummaryIn order to explore further the mechanism by which glycoprotein GPIIb-IIIa promotes platelet vessel wall interaction, platelet adhesion to subendothelium was studied in an annular chamber in which subendothelium from rabbit aorta was exposed at a shear rate of 2,600 s−1 to blood from patients with thrombasthenia. Perfusions were conducted for each of 5 exposure times (1 ,2,3, 5 and 10 min), and the percent surface coverage of the vessel segment with platelets in the contact (C) and spread (S) stage was determined. Increased values of platelet contact (C) were obtained in thrombasthenia at all exposure times; this finding is consistent with a defect in platelet spreadirg, based on a previously described kinetic model of platelet attachment to subendothelium. According to this model of attachment, increased values of platelet contact (C) at a single exposure time may be indicative of either a defect in spreading (S) or initial contact (C), but multiple exposures will result in increased contact only for defects which are related to defectiye platelet spreading (s).The results obtained over a broad range of exposure times provide more conclusive evidence that GPIIb-IIIa mediates platelet spreading than those previously obtained at single exposure times.

Automated Microdensitometry And Protein Assays As Measures For Platelet Adhesion And Aggregation On Collagen Coated Slides Under Controlled Flow Conditions

1979 ◽  
Author(s):  
R Muggli ◽  
H Baumgartner ◽  
Th Tschopp
Keyword(s):  
Surface Coverage ◽  
Platelet Surface ◽  
En Face ◽  
Uncoated Surface ◽  
Unit Surface ◽  
Perfusion Time ◽  
Flowing Blood ◽  
Controlled Flow ◽  
Protein Assays ◽  
Microscope Slides

Microscope slides were homogeneously coated over a length of 2 cm with a mixture of soluble and fibrillar collagen and exposed at 37°C and under laminar flow to citrated whole rabbit blood at a flow-rate of 100 ml/min. Surface coverage with platelets (adhesion) and platelet accumulations higher than about 5 μm in height (aggregation) were determined by automated microdensitometry of fuchsine stained ‘en face’ preparations. The platelet mass per unit surface was measured with a modified Lowry technique whose sensitivity was equivalent to 5×l05platelets. Platelet number, amount of protein and surface coverage with platelet accumulations correlated. After a perfusion time of 10 min thrombi up to 30 μm in height and oriented in the direction of flow had developed on the collagen coated area. Surface coverage with platelets was 75% and the amount of deposited protein 1.4 μg/mm2(2×l06platelets/mm2). On the uncoated surface single platelets predominated; the surface coverage was 20% and the density of platelets 8×104/mm2. Acetyl- salicylic acid at 100 μm decreased platelet aggregation by about 80% without affecting adhesion.The new parallel plate perfusion system offers rapid quantitation of platelet-surface and platelet-platelet interaction after exposure to flowing blood and iftay also be diagnostically useful.

SAMPL7 TrimerTrip Host-Guest Binding Poses and Binding Affinities from Spherical-Coordinates-Biased Simulations

2020 ◽  
Author(s):  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Computational Modelling ◽  
Free Energy Calculations ◽  
Energy Estimates ◽  
Binding Affinities ◽  
Spherical Coordinates ◽  
Collective Variables ◽  
Guest Binding ◽  
Starting Point ◽  
Sampl Challenge

Host-guest binding remains a major challenge in modern computational modelling. The newest 7<sup>th</sup> statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and predict the binding affinities in all three host-guest binding cases of the 6<sup>th</sup> SAMPL challenge. In this work, we employed the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The predicted binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations.

The Nature of Chemisorbed CO2 in Zeolite A

2019 ◽  
Author(s):  
Przemyslaw Rzepka ◽  
Zoltán Bacsik ◽  
Andrew J. Pell ◽  
Niklas Hedin ◽  
Aleksander Jaworski
Keyword(s):  
Solid State ◽  
Ir Spectroscopy ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Surface Coverage ◽  
Gas Mixtures ◽  
Mas Nmr ◽  
Significant Fraction ◽  
Zeolite A

Formation of CO<sub>3</sub><sup>2-</sup> and HCO<sub>3</sub><sup>-</sup> species without participation of the framework oxygen atoms upon chemisorption of CO<sub>2</sub> in zeolite |Na<sub>12</sub>|-A is revealed. The transfer of O and H atoms is very likely to have proceeded via the involvement of residual H<sub>2</sub>O or acid groups. A combined study by solid-state <sup>13</sup>C MAS NMR, quantum chemical calculations, and <i>in situ</i> IR spectroscopy showed that the chemisorption mainly occurred by the formation of HCO<sub>3</sub><sup>-</sup>. However, at a low surface coverage of physisorbed and acidic CO<sub>2</sub>, a significant fraction of the HCO<sub>3</sub><sup>-</sup> was deprotonated and transformed into CO<sub>3</sub><sup>2-</sup>. We expect that similar chemisorption of CO<sub>2</sub> would occur for low-silica zeolites and other basic silicates of interest for the capture of CO<sub>2</sub> from gas mixtures.

Pollutants in Organic Chemistry and Medicinal Chemistry Education Laboratory. Experimental and Machine Learning Studies

2020 ◽  
Vol 20 (9) ◽  
pp. 720-730
Author(s):  
Iker Montes-Bageneta ◽  
Urtzi Akesolo ◽  
Sara López ◽  
Maria Merino ◽  
Eneritz Anakabe ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Machine Learning ◽  
Chemistry Education ◽  
Organic Waste ◽  
Computational Modelling ◽  
University Education ◽  
Academic Factors ◽  
Academic Year ◽  
Statistical Analysis Software

Aims: Computational modelling may help us to detect the more important factors governing this process in order to optimize it. Background: The generation of hazardous organic waste in teaching and research laboratories poses a big problem that universities have to manage. Methods: In this work, we report on the experimental measurement of waste generation on the chemical education laboratories within our department. We measured the waste generated in the teaching laboratories of the Organic Chemistry Department II (UPV/EHU), in the second semester of the 2017/2018 academic year. Likewise, to know the anthropogenic and social factors related to the generation of waste, a questionnaire has been utilized. We focused on all students of Experimentation in Organic Chemistry (EOC) and Organic Chemistry II (OC2) subjects. It helped us to know their prior knowledge about waste, awareness of the problem of separate organic waste and the correct use of the containers. These results, together with the volumetric data, have been analyzed with statistical analysis software. We obtained two Perturbation-Theory Machine Learning (PTML) models including chemical, operational, and academic factors. The dataset analyzed included 6050 cases of laboratory practices vs. practices of reference. Results: These models predict the values of acetone waste with R2 = 0.88 and non-halogenated waste with R2 = 0.91. Conclusion: This work opens a new gate to the implementation of more sustainable techniques and a circular economy with the aim of improving the quality of university education processes.

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