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Surface Properties

Surface Properties refer to the physical, chemical, and biological characteristics of the outermost layer or interface of a material or substance.
These properties include wettability, adhesion, friction, roughness, and other features that influence interactions with the surrounding environment.
Researchers studying surface properties utilize a variety of analytical techniques, such as spectroscopy, microscopy, and contact angle measurements, to investigate and optimize these parameters for applications in fields like materials science, nanotechnology, and biolgoical engineering.
Understanding and manipulating surface properties is crucial for enhancing the performance of coatings, catalysts, sensors, and other advanced materials.

Most cited protocols related to «Surface Properties»

Predicting Protein-Protein Binding Affinities

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Publication 2015

Acclimatization Electrostatics Human Body Muscle Rigidity Mutation Surface Properties Trees

Evaluating HIV-1 Trimer Structural Flexibility

To estimate the degree of structural flexibility in the ligand-free HIV-1 trimer, we determined the average Cα RMSD distance for each residue position in the ligand-free trimer structure (Fig. 2d, e). The average Cα RMSD distance served as a proxy for structural plasticity and was computed between corresponding residues after optimal superimposition onto a set of 98 structures from the Protein Data Bank (PDB)^{67 (link)}. Each domain of the ligand-free trimer was considered separately and superimposed onto the set of structures using either the program TM-align^{68 (link)} or single value decomposition (SVD). To obtain the correct registry between corresponding residues, structural superimpositions were guided by amino acid sequence alignments when necessary. A total of 63 monomeric structures were used for superimpositions involving the gp120 domain (Supplementary Table 2). To generate Figure 1a (left), we used five representative gp120 structures; ligand-free clade A/E HIV-1 gp120 core_e (3TGT)^{19 (link)}, b12-bound gp120 (2NY7)^{21 (link)}, b13-bound gp120 (3IDX)^{3 (link)}, F105-bound gp120 (3HI1)^{3 (link)}, and VRC01-bound gp120 (3NGB)^{69 (link)} structures. For the gp41 domain we used a total of 35 structures from the PDB that included hexameric bundles as well as disordered peptides (Supplementary Table 2).
To understand the dynamic properties of ligand-free BG505 SOSIP.664, qualitative exchange profiles for observable peptides of SOSIP.664 after 3s were extracted from individual HDX -MS exchange plots^{70 (link)}. The average exchange values (0–75%) were substituted in the B-factor field for the observed peptides of closed, ligand-free BG505 SOSIP.664 coordinates and displayed within PyMol (Supplementary Figs. 1g and 2g). Non-observable peptides in the deuterium exchange experiment as well as peptides with missing electron density were excluded from the analysis.
Other residue-specific properties were calculated and shown Supplementary Table 1. These included residue depth^{71 (link)}, solvent-accessible surface area (SASA)⁷², sequence variability and hydrophobicity. Residue sequence variability was computed as the Shannon entropy for each residue position based on a representative set of 3,943 HIV-1 strains (Supplementary Figs. 1e and 2e). The electrostatic potential surfaces were generated using GRASP^{73 (link)}. The Pearson correlation coefficient and associated P-values (computed using two tailed t-test) were computed using the statistical package R. Residue-level and surface property analyses were carried out with coordinates that differed slightly from the deposited PDB (RMSD of the analyzed coordinates differed by 0.02 Å versus the deposited PDB for regions compared, and B-factors were identical.

Kwon Y.D., Pancera M., Acharya P., Georgiev I.S., Crooks E.T., Gorman J., Joyce M.G., Guttman M., Ma X., Narpala S., Soto C., Terry D.S., Yang Y., Zhou T., Ahlsen G., Bailer R.T., Chambers M., Chuang G.Y., Doria-Rose N.A., Druz A., Hallen M.A., Harned A., Kirys T., Louder M.K., O’Dell S., Ofek G., Osawa K., Prabhakaran M., Sastry M., Stewart-Jones G.B., Stuckey J., Thomas P.V., Tittley T., Williams C., Zhang B., Zhao H., Zhou Z., Donald B.R., Lee L.K., Zolla-Pazner S., Baxa U., Schön A., Freire E., Shapiro L., Lee K.K., Arthos J., Munro J.B., Blanchard S.C., Mothes W., Binley J.M., McDermott A.B., Mascola J.R, & Kwong P.D. (2015). Crystal structure, conformational fixation, and entry-related interactions of mature ligand-free HIV-1 Env. Nature structural & molecular biology, 22(7), 522-531.

Publication 2015

Amino Acid Sequence Complement Factor B Deuterium Electrons Electrostatics Entropy Figs HIV-1 HIV Envelope Protein gp120 Hydrogen Deuterium Exchange-Mass Spectrometry Ligands Peptides Protein Domain Solvents Strains Surface Properties

Decoding Semantic and Perceptual Features from MEG Activity

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Publication 2012

Character Equus caballus Homo sapiens Microtubule-Associated Proteins Nervousness Semantic Differential Surface Properties Vision

In Vivo Tibia Cortex Mechanical Assessment

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Publication 2013

Animals Biodent Bones Canis familiaris Cephalometry Condyle Consciousness Cortex, Cerebral Diaphyses General Anesthesia Head Homo sapiens Local Anesthesia Pain Periosteum Propofol Skin Sterility, Reproductive Surface Properties Tibia Tissues

Modeling Scintillation Light Output Variation

Our Monte Carlo simulation code was then used to model gamma interactions occurring at different depths in the scintillator and to generate light pulses to be recorded by a photodetector. In this work, all parameters were chosen to simulate a LSO crystal as employed in the experimental setup used for validation. The bulk LSO material was modelled with an index of refraction of 1.82, an absorption length of 300 mm and a scattering length of 256 mm (Rothfuss et al., 2004 ). For each gamma interaction, the energy of the interaction was randomly selected following a distribution derived from an energy spectrum measured with a 2 × 2 × 20 mm³ LSO polished (Figure 2).
The corresponding number of scintillation photons was then calculated assuming a light yield of 25 photons/keV (Moszynski et al., 1997 ). The measured energy distribution accounts for the statistical variation introduced by the light yield of the scintillator and the quantum efficiency of the detector. We considered only photoelectric effect and Compton scattering. For each gamma interaction visible photons were emitted isotropically, creating a light pulse. The fate of these individual photons was tracked. Appropriate wavelength and time of emission for each photon were assigned based on scintillator properties. Scintillators produce light pulses with an exponential decay (~40 ns for LSO)(Melcher and Schweitzer, 1992 ), thus the emission times assigned to individual photons followed an exponential distribution with a 40 ns decay. Scintillator rise time was ignored. The emission spectrum of LSO was measured on crystals from the same bulk material as that used for experiments with time resolved fluorescence spectroscopy (TRFS) (Yang et al., 2009 (link)). The wavelength of the photons generated was selected by randomly sampling the measured emission spectrum.
On encountering the edges of the scintillator, photons may be reflected or transmitted (Figure 3a). If a photon reaches the scintillator/photodetector boundary, it may be transmitted and detected, or reflected. A fraction of photons also escapes the crystal. The photodetector was modelled by its quantum efficiency spectrum. For the simulations presented here, the quantum efficiency (QE) of a Hamamatsu R6231 PMT was used. The size of the photodetector was set to match the size of the crystal. The reflection properties of the crystal surfaces were modelled using the LUTs computed from the AFM surface measurements. One end of the crystal was modelled as a polished surface in contact with optical grease (index of refraction 1.5) whereas all the other faces were modelled in contact with air (both in the case of an external reflector such as Teflon tape and no reflector) and either polished or rough depending on the type of crystal simulated. In some simulations, an external diffuse reflector was modelled by its reflection coefficient of 0.97, corresponding to a diffuse reflector such as Teflon tape. This reflector was assumed to be Lambertian and so the direction of reflected photons followed a Lambertian distribution. Photons were refracted when they re-entered the crystal. Arrival times and wavelengths of detected photons were recorded and used to generate simulated light pulses and spectra.
Different configurations were investigated: 2 × 2 × 20 mm³ and 5 × 5 × 20 mm³ “rough” crystals (ground finish surfaces) were simulated without and with reflector using the LUTs computed from the “rough” surface samples measured on such crystals. 2 × 2 × 20 mm³ LSO polished crystals were also simulated. For each crystal, 500 light pulses were generated in 1.5 mm bins at five different depths every 4 mm starting 2 mm away from the photodetector face that is at 0 mm. At each depth, the energies were histogrammed and the photopeak positions were extracted from the energy spectra to characterize the light output variation with depth. The maximum light output was defined as the photopeak position at the irradiation depth closest to the photodetector, 2 mm. All maximum light output values were normalized by the maximum light output of the polished crystal with reflector (expected to be the highest).

Roncali E, & Cherry S.R. (2013). Simulation of light transport in scintillators based on 3D characterization of crystal surfaces. Physics in medicine and biology, 58(7), 2185-2198.

Publication 2013

Dietary Fiber Face Fluorescence Spectroscopy Gamma Rays Light Lutein lutetium orthosilicate Pulse Rate Pulses Radiotherapy Surface Properties Teflon

Most recents protocols related to «Surface Properties»

Antibacterial Dental Implant Surface Modification

Not available on PMC !

Example 2

As discussed herein above, the disclosed methods improve the antiseptic properties of a dental implant without using charged metallic ions via conversion of the nitrogen moieties in titanium nitride surface to a positively charged quaternary ammonium via a Menschutkin reaction.

To prepare the antibacterial quaternized TiN surface, an implant which has been coated with TiN was used. The implant was cleaned to improve yield. The implant was washed with two solvents in sequence, acetone and isopropanol, to remove any dust particulate and other residue. The native oxide layer was removed by sonicating in 1:10 HCl:deionized water for 1 minute. This treatment additionally removes any residue that may not have been removed by the solvents. Acetonitrile was used as the solvent; however, any solvent may be used with preference for polar solvents giving improved reaction times (Stanger K., et al. J Org Chem. 2007 72(25):9663-8; Harfenist M., et al. J Am Chem Soc 1957 79(16):4356-4358). An excess of allyl bromide was added to the solvent and continuously stirred. The sample was then submerged in the solution, and full reaction of the surface occurred within about 60 minutes, as confirmed by contact angle measurement. A reference was also measured by submerging in solvent for the duration with no reactant to ensure any changes in surface properties was due to the quaternization.

TABLE 2

SampleContact Angle (°)

As-deposited TiN<6

In solvent 2 hrs (no reaction)16 ± 2

Allyl bromide 30 minutes67 ± 1

Allyl bromide 60 minutes72 ± 3

Allyl bromide 120 minutes71 ± 2

Without wishing to be bound by a particular theory, the increased hydrophobicity of the treated surfaces can be due to the presence of the allyl groups on the surface which will impart some hydrophobicity. The contact angle measurements provide information on whether or not a reaction has occurred and whether it has saturated.

The biocidal activity was tested using live bacteria cultures from a patient's mouth, which provides the full flora to act against rather than targeting an individual strain of bacteria. The bacteria was incubated on the sample surface using several bacteria film thicknesses. The thickness is defined by keeping the same interaction surface area while varying the volume of bacteria solution added. Across two separate patients and several separate growths, within 4 hours 40-50% reduction in bacteria unit counts were observed for quaternized TiN as compared to traditional Titanium implants, outperforming traditional TiN coatings. FIG. 4 shows for two separate patients a set of typical bacteria growth result of the quaternized samples. The exact efficiency varies, as each patient has different flora which varies depending on environmental factors such as hygiene, diet, and familial history.

Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill in the art to which the disclosed invention belongs. Publications cited herein and the materials for which they are cited are specifically incorporated by reference.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the disclosure. Other aspects of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

US11864964B2. Quarternized titanium-nitride anti-bacterial coating for dental implants (2024-01-09). University of Florida Research Foundation, Inc. [US]. Inventors: Josephine F. Esquivel-Upshaw [US], Fan Ren [US], Patrick Carey [US], Arthur E. Clark, Jr. [US], Christopher D. Batich [US].

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Patent 2024

Acetone acetonitrile allyl bromide Ammonium Anti-Bacterial Agents Anti-Infective Agents, Local Bacteria Diet Implant, Dental Ions Isopropyl Alcohol Metals Nitrogen Oral Cavity Oxides Patients Solvents Strains Surface Properties Titanium titanium nitride

Machine Learning-based Air Quality Forecasting in the PNW

In the PNW, currently there are 47 AQS sites with O₃ observations, 138 sites with PM2.5 observations. Similar to the ML modeling framework for Kennewick, the training dataset for this multi-site ML models included the previous day's observed O₃ or PM2.5 concentrations, time information (hour, weekday, month represented as factors), and hourly meteorological forecast data from twice-daily ensemble WRF forecasts extracted at each AQS site. The WRF meteorology data was provided by the twice-daily ensemble forecasts with 4 km horizontal resolution, produced by the University of Washington (UW, https://a.atmos.washington.edu/mm5rt/ensembles/).
The UW ensemble system applies multiple physical parameterizations and surface properties to the WRF model simulations, and the ensemble forecasts could improve the forecast skill for some cases (Grimit and Mass, 2002 (link); Mass et al., 2003 (link); Eckel and Mass, 2005 (link)). To utilize the varying settings for the meteorology simulations, we input the multi-member WRF ensemble forecasts for the air quality forecasts in the PNW.
The evaluation of O₃ predictions in this paper covers May to September from 2017 to 2020 and PM2.5 predictions cover two seasons, wildfire season (May to September) and cold season (November to February) from 2017 to 2020. While wildfires can affect both O₃ and PM2.5 concentrations significantly, wood burning from stoves during cold season is a significant source of PM2.5 in populated areas, so we look at only PM2.5 for cold season. To identify the characteristics of each individual site, the models are trained for each monitoring site with archived 4 km WRF forecasts and observations. For the model evaluation, we used the archived operational WRF data, which is a single ensemble WRF member from UW forecasts. The observations and archived WRF data are available at 30 sites for O₃ and more than 100 sites for PM2.5, and there are 12 sites where both O₃ and PM2.5 are measured.

Fan K., Dhammapala R., Harrington K., Lamb B, & Lee Y. (2023). Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest. Frontiers in Big Data, 6, 1124148.

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Publication 2023

Cold Temperature Pemphigus and fogo selvagem Physical Examination Surface Properties Wildfires

Biosorption Kinetics and Isotherms of Methylene Blue

The experiments of MB biosorption isotherm studies were performed under shaking in 250 mL Erlenmeyer flasks holding 100 mL of various initial MB concentrations (10–200 ppm), and then 0.05 g of the prepared bio-sorbent material was added at the optimum pH value (8.5). Following the completion of each set of experiments, the loaded bio-sorbent material P. alcaliphila NEWG-2 with MB was separated from solutions by centrifugation at 10000 rpm for 20 min, and the remaining MB concentrations were determined using a UV–Vis spectrometer at 668 nm. The MB removal % from the aqueous solution was estimated by Eq. (2):

R % = \frac{C_{i} - C_{e}}{C_{i}} * 100

where C_i is the initial concentration and C_e is the final concentration of MB (mg/L).
Moreover, the amount of MB dye bio-sorbed (qe, mg/g) onto the surface of the prepared bio-sorbent material was estimated by Eq. (3);

q_{e} = (C_{0} - C_{e}) \frac{V}{M}

where q_e is biosorption capacity, C₀ is the initial MB concentration (mg/l), C_e is the MB concentration at equilibrium (mg/l), M (g) is the mass of the prepared bio-sorbent materia used l, and V (L) is the volume MB solutions.
The equilibrium biosorption isotherm models are a very important step in the design of the biosorption system, as they reveal the adsorbent's capability. The biosorption isotherm plots are characterized by certain constants that definite the affinity of the bio-sorbent and its surface properties. They also, describe equilibrium relationships between bio-sorbate and bio-sorbent (the proportion of the bio-sorbed amount and the remaining amount in the solution at equilibrium)^{37 (link)}. To understand the biosorption isotherms, three common equilibrium isotherm models were fitted to the obtained results. The Langmuir, Freundlich, and Temkin biosorption slopes, intercepts, and constants assessed from the isotherms plots and their correlation coefficients (R²) are illustrated in Table 2.Table 2

Equations of the isotherm sorption models for the biosorption of MB onto the cell biomass of the bio-sorbent P. alcaliphila NEWG-2.

Biosorption model	Equation	Parameter
Langmuir	C_e/q_e = 1/q_mK + C_e/q_m	q_e is the amount of MB (mg/g) bio-sorbed at equilibrium, q_m is the maximum capacity of the monolayer (mg/g), K is the Langmuir constant (L/mg), and C_e is the MB concentration at equilibrium (mg/L)
Freundlich	Ln q_e = ln k_f + 1/n_f ln c_e	q_e is the amount of MB bio-sorbed at equilibrium (mg/g); C_e is the concentration of MB at equilibrium (mg/L); and n_f and K_F are Freundlich constants of the biosorption intensity and capacity, respectively
Temkin	q_e = B ln KT ‏+ B ln C_e	K_T is the Temkin constant referring to maximum equilibrium binding energy and B is the Temkin constant of biosorption heat

The key parameters of the Langmuir isotherm model can be determined by the Langmuir separation factor, or an equilibrium parameter, R_L, which is calculated as follows (Eq. 4):

R_{L} = \frac{1}{1 + b C_{0}}

where C₀ is the initial MB concentration (ppm); b is Langmuir's constant; and R_L indicates the type of isotherm. If R_L values between 0 and 1 indicate a favorable biosorption, R_L > 1 indicates an unfavorable biosorption. Also, when R_L = 0 indicates irreversible biosorption, R_L = 1 showed a linear biosorption process.

Ghoniem A.A., Moussa Z., Alenzi A.M., Alotaibi A.S., Fakhry H., El-Khateeb A.Y., Saber W.I, & Elsayed A. (2023). Pseudomonas alcaliphila NEWG-2 as biosorbent agent for methylene blue dye: optimization, equilibrium isotherms, and kinetic processes. Scientific Reports, 13, 3678.

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Publication 2023

Cells Centrifugation Surface Properties

Characterization of Polyester Fabric Photocatalysis

Characterization
analysis on polyester fabric was carried out by the optical angle
test, SEM, and FTIR analysis. Analysis of the optical angle test was
carried out with (Specification) functioned to determine the contact
angle on the fabric. The resulting data will be used to determine
the surface properties of the material. The distilled water is dripped
at a distance of 1 cm from the surface of the cloth. Next, the contact
angle on the fabric was determined. Scanning electron microscopy (SEM,
Hitachi SU-3500, Tokyo, Japan) analysis with a voltage of 3.00 kV
at a magnification of 2000× was performed to determine the surface
morphology of the fabric. The photocatalysts were further characterized
using Fourier-transform infrared spectroscopy (FTIR, PerkinElmer Spectrum
100, Massachusetts, USA) to determine the functional groups in the
composites. In determining this performance, a solution of methylene
blue dye is needed, which is prepared with various concentrations
of 30 and 300 ppm. The dye was then dripped one drop of methylene
blue solution each vertically using a 1 cm burette on a cloth on a
flat surface and allowed to stand for 3 min. The cloth was then irradiated
with a mercury lamp (HPL-N 125 W Philips) at a distance of 10 cm and
a sample image was taken every 60 min for 6 h using a smartphone camera.
Furthermore, the percentage decrease in the dye concentration was
calculated using a calorimetry-based digital image method. The camera
Redmi Note 10 (Xiaomi Inc.), with the characteristics of Table 2, was used to record
images for viewing color-based digital images. ImageJ 1.52a (NIH image),
Excel 2019 (Microsoft Inc.), and Minitab 19 (Minitab Inc.) were used
for digital image data processing.

Eddy D.R., Luthfiah A., Permana M.D., Deawati Y., Firdaus M.L., Rahayu I, & Izumi Y. (2023). Rapid Probing of Self-Cleaning Activity on Polyester Coated by Titania–Natural Silica Nanocomposite Using Digital Image-Based Colorimetry. ACS Omega, 8(8), 7858-7867.

Publication 2023

A-A-1 antibiotic Calorimetry Mercury Polyesters Scanning Electron Microscopy Spectroscopy, Fourier Transform Infrared Surface Properties Vision Tests

Wetting Properties of Coated Wood

The wetting properties of wood surface with coating were estimated on the basis of the behavior of coating droplets after application on the surfaces of 3 uncoated samples (L = 50 mm). Five 5 µL-droplets of coating were applied on each sample with optical goniometer Theta (Biolin Scientific Oy, Espoo, Finland). The camera of goniometer enabled to record the motion of the droplet and to measure the CA of droplet following the Young–Laplace analysis^{24 (link)} continuously during first 60 s after application. The final CA was measured 2 h after droplet application.
After application on wood, the spilling of the coating droplets was determined by applying five 20 μL-droplets of coating from 20 mm above the sample surface using a pipette fixed on the stander. The spilling of the droplets 1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 45, 60 and 7200 s after the application was recorded from above with a photo camera (D5600, Nikon, Tokyo, Japan). The photos were analyzed by measuring the spilling area (in mm²) of the droplets using the Fiji software (ImageJ 1.46d, Bethesda, Maryland, USA)^{25 (link)}.

Žigon J., Centa U.G., Remškar M, & Humar M. (2023). Application and characterization of a novel PVDF-HFP/PVP polymer composite with MoO3 nanowires as a protective coating for wood. Scientific Reports, 13, 3429.

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Publication 2023

Surface Properties

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S 4800 by Hitachi

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The S-4800 is a high-resolution scanning electron microscope (SEM) manufactured by Hitachi. It provides a range of imaging and analytical capabilities for various applications. The S-4800 utilizes a field emission electron gun to generate high-quality, high-resolution images of samples.

Noa 148 by Norland Products

The NOA 148 is a contact angle measurement instrument designed for determining the wettability and surface energy of solid samples. It utilizes a high-resolution camera and precise liquid dispensing system to capture and analyze the contact angle formed between a liquid and the surface of a solid material.

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What are the key properties that determine a material's surface characteristics?

The key surface properties that influence a material's interaction with its environment include wettability (how easily a liquid spreads on the surface), adhesion (the ability to bind to other materials), friction (the force resisting motion between surfaces), and roughness (the microscopic texture of the surface). Understanding and controlling these parameters is crucial for optimizing the performance of coatings, catalysts, sensors, and other advanced materials.

How can researchers effectively study and measure surface properties?

Researchers utilize a variety of analytical techniques to investigate surface properties, such as spectroscopy (to determine chemical composition), microscopy (to visualize surface topography), and contact angle measurements (to quantify wettability). These tools provide valuable insights that can be used to engineer surfaces with desirable characteristics for specific applications in fields like materials science, nanotechnology, and biological engineering.

What are some common challenges in working with surface properties?

One of the key challenges in working with surface properties is ensuring reproducibility, as small variations in experimental conditions can significantly impact the results. Additionally, optimizing surface properties often requires a delicate balance, as enhancing one parameter (e.g., adhesion) may come at the expense of another (e.g., friction). Researchers must carefully consider these trade-offs when designing and evaluating surface-based materials and technologies.

How can PubCompare.ai assist in surface properties research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoit critical insights. The platform's AI-driven analysis can help identify the most effective protocols related to surface properties for your specific research goals, highlighting key differences in protocol effectiveness and enabling you to choose the best option for reproducibility and accuracy. This can save time, reduce frustration, and enhance the quality of your surface properties research.

Are there different types or variations of surface properties?

Yes, there are various types and variations of surface properties that researchers may need to consider. For example, in addition to the core properties like wettability and roughness, other important surface characteristics include surface energy, charge, and reactivity. The specific surface properties of interest will depend on the material, application, and research objectives. Understanding these nuances is key to optimizing surface-based technologies.

More about "Surface Properties"

Surface characteristics, surface science, interface properties, wettability, adhesion, friction, roughness, spectroscopy, microscopy, contact angle, materials science, nanotechnology, bioengineering, coatings, catalysts, sensors, S-4800 SEM, NOA 148 ellipsometer, D8 Advance XRD, ESCALAB 250Xi XPS, JEM-2100F TEM, Zetasizer Nano ZS, ASAP 2020 surface area analyzer, JEM-2100 TEM, EZ1590 flow cytometer, Ultima IV UV-Vis spectrometer.