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Resources 2018-01-30T11:42:38+00:00

Pre-CourseVIDEOS List:

Gas Adsorption videos:

Courses referenced:

ASAP 2020 Physisorption
ASAP 24XX
TriStar 30XX
Gemini VII 23XX
3Flex 3500

 
 

Particle Size Video:

Courses referenced:

Saturn DigiSizer II

Saturn DigiSizer II High-Definition Digital Particle Size Analyzer

 

Reporting Software and Data Reduction Videos:

Courses referenced:

ASAP 2020 Physisorption
ASAP 24XX
TriStar 30XX
Gemini VII 23XX
3Flex 3500

 

MicroActive Reporting Software – Isotherm

 

MicroActive Reporting Software – BET Microporous

 

MicroActive Reporting Software – t-plot Microporous

 

MicroActive Reporting Software – t-plot Mesoporous

 

MicroActive Reporting Software – BJH Silica

 

MicroActive Reporting Software – DFT

ARTICLES and RESEARCH Papers List:

The NanoPlus includes a temperature gradient feature for size and zeta potential. This unique feature allows the researcher to further utilize the built-in Peltier Thermo-electric controller for temperature studies. The sample cell in the NanoPlus can be held at a specific temperature between 5°C to 90°C or a gradient of increasing or decreasing temperatures in 0.1°C increments or greater. This is ideal for determining the melting point, or denaturing point, of proteins using the NanoPlus.

To set up a temperature gradient for protein analysis, set the “Manual Temperatures Setting” under “Measurement Parameters” to “Gradient” in the Size SOP Designer. Then complete the “Gradient Temperature (°C)” table with the start temperature, end temperature, interval, and equilibration time in seconds. This setting tells the NanoPlus to collect size data at each temperature point and save each point as a separate data file in the “Size Analysis” database.

Once the analysis is complete, the software also automatically generates a “Temperature Gradient Analysis” file that can be opened under “Size Analysis.” The file will have the ending .tgr for temperature gradient and, when opened, will display Size (nm) versus Temperature (°C). The Cumulants value will be used for the size points.

A sample of bovine albumin serum (BSA) is tested using the temperature gradient function. The test was repeated and in both cases, the melting point was found to be at 68.0°C.

The Micromeritics 3Flex design includes metal seals, hard seal valves, and both a Pirani and cold cathode vacuum gauges. The hardware is complemented by the state of the art software which continuously monitors the status of the instrument. These hardware and software feautures allow 3Flex users to perform adsorption experiments that were previously unavailable in multi-port instruments. 3Flex users may characterize three samples simultaneously and each sample may use a different adsorptive.

For example:

  1. Zeolite 4A on sample port 1 using hydrogen,
  2. Zeolite 13X on sample port 2 using nitrogen,
  3. Zeolite 5A on sample port 3 using argon.

The high-performance 3Flex vacuum system coupled with minimal system volumes allows for rapid gas supply changes while maintaining superior levels of cleanliness.  This new capability is a standard feature available for all 3Flex users.

For additional information on the 3Flex or other Micromeritics instruments please contact your local representative.

MicroActive 2.0 and MicroActive for 3Flex include a powerful utility for including the pore size distribution from mercury porosimetry analyses with pore size distributions calculated from gas adsorption isotherms. This new import function allows users to rapidly view micro, meso, and macro pore size distributions in one easy to use application.

Usage:

This new import function has been added that allows users to rapidly include pore size distributions from other devices to be included into the standard Micromeritics sample file (SMP file). The pore size data from the external source such as a mercury porosimeter is imported from a simple text file. The first line of the text file includes a description, size units, quantity units, and type of data with the following format.

Figure 1: Example file format for importing mercury porosimetry data.

The porosimetry data then follows with the pore width in the first column and the quantity in the second column. An example of the format is given in figure 1. The intrusion data must be strictly increasing and extrusion data must be strictly decreasing.

Description: Used to identify the imported data and as the graph legend

Pore Width Units: Ångstroms – A, nanometers – nm, or micrometers – um

Quantity Units: ml/g or cm3/g

Type: Incremental or cumulative

The incremental or cumulative data is available from the Micromeritics AutoPore 9500 application. The data may be copied by right-clicking on either the cumulative or incremental pore volume graphs generated by the AutoPore reports and then using the Copy as text option. A file to be imported is restricted to either intrusion or extrusion data. However, multiple files may be imported to allow the inclusion of both intrusion and extrusion data.

Importing pore volume data:

The pore volume data may be integrated into a sample file (SMP file) using the Import … function located on the Report Options tab in the Advanced view of the SMP file (figure 2).

Figure 2: The import utility is found on the Advanced tab of the SMP file.

The Import button opens a dialog, figure 3 and lists any pore volume data that have been previously imported to the SMP file and each set of pore volume data is listed by description. Additional data be be imported or removed from the SMP using the Select Imported Overlays dialog.

import

Figure 3: The import file selector lists each pore size distribution by description.

If an error is encountered while importing the mercury porosimetry data, a descriptive message is displayed that includes a description of the correct file format, figure 4.

Figure 4: Error message for improperly formatted pore volume data file.

The imported data may then be used as an overlay with cumulative, differential, or log differential pore size distributions. The overlay is selected by editing the options for the BJH, Dollimore-Heal, Horvath-Kawazoe, and DFT pore size reports. Each report has the option for including an overlay and the Imported Data may be selected as seen in figure 5.

Figure 5: Imported data may be used as an overlay in cumulative, differential, and log-differential pore size distributions.

Example:

Commercial catalysts are commonly analyzed using nitrogen adsorption to determine surface area and porosity. The pore size distribution may then be calculated from the nitrogen adsorption isotherm using BJH. Mercury porosimetry is also commonly employed to determine the pore volume distribution of catalysts and the pore volume distribution may overlaid with the distribution calculated from the nitrogen adsorption isotherm.

Figure 6: Overlay of BJH desorption, mercury intrusion, and mercury extrusion log differential pore size distributions for alumina pellets

In this example, the Micromeritics chemisorption reference material a 0.5 wt% platinum on alumina sample was characterized using nitrogen adsorption and mercury porosimetry. The nitrogen adsorption and desorption isotherms were used to calculate the pore size distribution using the BJH method. Both mercury intrusion and extrusion data were imported into the gas adsorption SMP file (figure 6).

The overlay of the pore size distributions is given if figure 6. Based upon this simple overlay of the log differential pore volume distribution, the size distribution (figure 6 in red) calculated from the desorption branch of the isotherm agrees well with the pores size distribution (in green) determined by mercury intrusion porosimetry.

Summary:

MicroActive version 2.0 provides new and unique capabilities for integrating pore volume data from mercury intrusion analyses with the pore size distributions calculated from gas adsorption isotherms. The MicroActive software is compatible with SMP files from Micromeritics broad range of gas adsorption instruments and provides a new capability for examining new or historical pore volume distributions.

The selection of data used for fitting BET surface area is often an easy process using the MicroActive software. A broad range of isotherm data is selected and then the BET fit may be further refined to yield the specific surface area of a material. The process for data selection and optimization has been fully automated using the BET surface area rules advocated by Professor Jean Rouquerol, CNRS Marseille, FR. Prof. Rouquerol recommends the following criteria:

  1. A linear fit to the (BET) transformed data should be obtained and this is a common practice for calculating specific surface area.
  2. The BET “C” constant must be positive ( C > 0 ).  This is also common practice for calculating specific surface area.
  3. The Rouquerol transform  n_{ads}( 1 - p/p^o) should be increasing with p/p^o for the data selected to calculate the BET parameters.
  4. The monolayer capacity – n_m should be within the limits of data that were used to fit the BET parameters.
  5. The value of 1/(\sqrt{C} + 1) \approx p/p^o at the monolayer capacity.

Rules 3, 4, and 5 have been contributed by Prof. Rouquerol and these rules allow for the automatic selection of points to calculate the BET surface area.  Using rule three, n_{ads}( 1 - p/p^o) is monotonically increasing with p/p^o.  This provides a clear basis for determining the maximum values of p/p^o and n_{ads} as this plot (the Rouquerol Transform) has a peak maximum corresponding to the maximum values to be used in the BET transform.

Rule four is used to confirm that the calculated value of the monolayer capacity n_m falls within the range of data used to calculate the BET parameters.  This confirms that the monolayer capacity has not been extrapolated.

Rule five 1/(\sqrt{C} + 1) \approx p/p^o provides a check for consistency and may be used to further optimize the selection of points used to calculate the BET fit.  The value of 1/(\sqrt{C} + 1) should approach the relative pressure calculated from the BET transform at the monolayer capacity.

This new report is available for use with MicroActive 2.0 and the MicroActive included with: GeminiTriStar II PlusASAP 2460, and 3Flex.   Please contact your local Micromeritics representative to obtain this new report or send a request to techsupport.micromeritics.com.

The automatic selection of points for BET surface area has been tested using a broad range of materials.  Below is an example of the automated selection and optimization for a Fluid Cracking Catalyst (FCC).  It is often difficult to select the proper range for the BET calculation since it is a combination of zeolite (faujasite) and clay binder and the microporous component (zeolite) may often cause the BET “C” value to be negative.  This new report validates the optimized range against the previously given rules and gives both the summary data and the results of the rule checking.

Calculating the BET surface area of metal organic frameworks (MOFs) may be equally challenging. Below is an example of the automatically optimized BET fit for Basolite C300.

The automatic selection is also effective for non-microporous materials such as silica and titania (see below)


silica

These five rules are equally applicable to microporous carbons and an example is given below.

The instruments used to obtain the isotherm data may be viewed at www.micromeritics.com.

Technical support for Micromeritics users is freely available through techsupport.micromeritics.com.

Accredited training and education for Micromeritics instruments users are available through www.micro.edu.

The Micromeritics 3Flex has the added capability to collect and display pressure & quantity adsorbed versus time measurements. Data are collected every half second. This application note is a tutorial on how to collect the data being transmitted over the Ethernet cable used for instrument communication using a client such as PuTTY or HyperTerminal. Also, it will be shown how the captured data can easily be handled using scripts created in languages such as Python.

Installing the PuTTY client:

PuTTY is an open source (free to download) SSH and telnet client that can easily be used to access all of the transient data output by the 3FLEX 3500 instrument. Since PuTTY is open source and easy to download, it will be the client discussed in this tutorial. No program installation is required. The .exe file runs directly from a Windows desktop. The software can be downloaded online (only save the putty.exe file):

Collecting the pressure versus time data:

In order to connect to the instrument, the following items have to be configured:

  1. Input 3FLEX IP address, found in the Unit Configuration, into the PuTTY software
  2. Input 54000 in the ‘Port’ field
  3. Select ‘Raw’ connection type
  4. Save session settings (optional)
  5. To save the data, go to the ‘Logging’ settings (see Figure 3).
    • Select ‘All session output’
    • Select destination (using ‘Browse…’) to save file and create a .txt file (helpful to name this file the same as your sample file to be analyzed)
    • Click ‘Open’ to start collecting the data (note: this program can be started before the 3FLEX starts collecting data—it will be ‘waiting’ for when data output from the 3FLEX begins).


Using the data:

The text file containing the transient data can be accessed using any means that accepts tab separated values. Possibilities include Notepad, Microsoft Excel (or other spreadsheet software), or programming languages that read .txt or .xls files. Within programming languages, MATLAB®, Octave, and Python may be a few that come to mind. MATLAB® has the xlsread() function and Octave has the textread() function which are easy to use. However, the use of Python is chosen as the focus of this application note since Python is free to use, well supported in terms of online documentation / user forums, and the use of python code is also possible within the 3FLEX software (sample files). Please refer to Appendix I for a description of all of the data contained within the saved file. Refer to Appendix II for installing the proper components of Python, and refer to Appendix III for example code and how to execute the use of Python to view transient data on demand.

Description of all columns in colected data file:

The data file generated during this collection process has 58 columns of tab-delimited, or separated, values. When viewed in Microsoft Excel or other spreadsheet program, the data occupy columns ‘A’ though ‘BF’. The identity of each of these columns of data is given below:

Python installation components:

In order to operate Python in a way that on-demand data can be viewed, 3 components need to be installed: Python 3.2, NumPY (for numerical functions), and MatPlotLIB (for graphical display of the data).

Installing Python 3.2:

Installation of Python needs to be done first. Python version 3.2.3 can be downloaded from python.org.

Depending on your computer system, appropriate versions may vary, but the following file should be compatible with most, if not all, computers that operate the 3FLEX instrument:

Installing NumPY:

NumPY can be installed next and contains numerous numerical functions and libraries which are needed in order to properly handle the transient data extracted from the .txt file collected during an analysis. NumPY version 1.6.2 should be downloaded to be compatible with the installed version of Python.

INSTALLING MatPlotLIB

The last necessary component of the installation is MatPlotLIB. Version1.2.1 is compatible with the previously mentioned / installed software and can be downloaded.

SAMPLE PYTHON CODE

The below is example code that was used to produce the data shown in Figure A3.1. This is the most basic code needed to see transient data, but the flexibility exists to script a more detailed file—allowing for flexibility such as comparing pressure or quantity adsorbed between ports, generating a separate figure for each port, etc.

import numpy as np
import matplotlib.pyplot as plt
myFile = 'carbon.txt'
data = np.genfromtxt(myFile, skip_header=2, skip_footer = 1)
################
# Time (minutes)
################
x=data[:,3]
x = x/1000/60
# Sample pressure PORT 1(torr)
y=data[:,4]
plt.plot(x,y,'ko')
plt.ylabel('Pressure (torr)')
plt.xlabel('time (minutes)')
plt.show()

Example pressure data obtained from transient data recording.

Sample of transient data displayed in a text editor. Note the ASCII art and labels of the first two rows of data—which need to be excluded from the numerical data.

While this code is very basic and only begins to explore the possible script configurations, scripts with greater power and flexibility can readily be created. Micromeritics’ applications specialists would be glad to discuss and support creating custom scripts for individual applications.

Learning Center RESEARCH PROJECTS:

Carbon Dioxide Characterization of Carbons with the TriStar II 3020

Part I: Characterization of Supported Palladium, Hydrogen Sorption

Characterization of Eucalypt Wood by Mercury Porosimetry

Pulse Chemisorption with AutoChem II 2920 Isopropylamine on Zeolites

TPR,TPO and TPD Examination of Cu0.15Ce0.85O2-y Mixed Oxide Catalyst Prepared by co-precipitation synthesis