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Automated MP Determinations
Application Note #5
Introduction
OptiMelt was specifically designed to detect and determine melting points with
completely unattended operation. What sets this instrument apart from its
competition is its reliance on a "built-in digital camera" and sophisticated Digital
Image Processing (DIP) to detect and determine melting points.
Figure 1. View of the OptiMelt's heating chamber. A built-in
camera faces the samples; a digital image processor calculates
melting points and melting ranges.
A very important advantage of the OptiMelt system is its capability to determine
melting points automatically while simultaneously visualizing the samples. If
required, the capillaries can be viewed at any time, and the melting points can be
determined visually. The choice between a time-consuming visual determination
and a faster blind automated method is eliminated. Visualization is important for
colored or problematic samples, for chemists trying to reproduce published visual
observations, and for synthetic chemists generating new, intermediate or exotic
compounds.
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Figure 2. OptiMelt offers the option to determine melting points
automatically while simultaneously visualizing the samples.
Automation offers significant time saving advantages for routine determinations
of melting points in production, research and educational settings.
This application note describes the automation capabilities of OptiMelt and
compares its Digital Imaging approach to the more primitive optical transmission
and reflection methodologies used by its competitors.
Automation Techniques
Most modern automated melting point measurement instruments rely on changes
in optical properties of the solid samples during the heating ramp to detect and
estimate melting points and ranges.
The three optical techniques used to automatically detect melting point
transitions are: 1) Transmission, 2) Reflection, 3) Digital Imaging.
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Figure 3. Schematic representation of the transmission method.
The principle of operation of the transmission method is simple. The capillary
tube standing in a heating block is illuminated from the front with a source of IR
light (i.e. LED). A small light channel, drilled into the metal block (directly behind
the capillary slot), optically connects the emitter to a photodiode located behind
the heater block. A solid sample effectively blocks all light from getting to the
photodetector (i.e. 0 % transmission). During the heating process, the light
intensity measured by the photocell increases. At a certain transmittance (40 %
typical) the sample is deemed to have melted. The melting range is often
determined by measuring the temperatures at which the transmission goes
above 10 %, and then again when it reaches 90 % (note: thresholds are
generally user-programmable). Calibration of the transmission is required and is
performed using a capillary with solid sample (0 %) followed by an empty tube
(100 %). The transmission technique requires that the melted sample not be
opaque, be free of decomposition, and that the compression (density) be uniform
from sample to sample to get good reproducibility.
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Figure 4. Schematic representation of the reflection method.
In a reflection setup, the melting point transition is detected by measuring
reflected infrared light. A light source illuminates the front face of the capillary
sample and the light reflected "by the crystals" is detected and measured by a
photodetector. In order to thermally decouple the heating block from the detector
and to block light reflected from surfaces other than the crystals, fine fiber optics
are often used to deliver the light and to collect the reflected radiation. Dark,
opaque and decomposing samples are often compatible with this method of
detection.
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Figure 5. Schematic representation of the digital imaging method.
Digital Imaging is a revolutionary detection scheme found in every OptiMelt
system manufactured by Stanford Research Systems. A built-in "digital camera"
continuously captures real-time images of the illuminated samples throughout the
heating ramp, and sophisticated Digital Image Processing (DIP) technology
detects and determines melting points from the analysis of the stored images.
The unattended melting points and melting point ranges determined by the
OptiMelt system naturally match your visual observations and provide a dramatic
improvement over the data generated by units relying on optical transmission
and reflection techniques. The high-resolution camera can easily detect and
interpret minute changes in the optical characteristics of the capillary samples in
a manner very similar to your own eyes. This effectively eliminates the need for
the user to be present during the analysis and avoids the subjectivity which is
often associated to every visual melting point determination. Colored, opaque
and thermally-labile samples can often be analyzed in this fashion.
The three automation techniques described above are generally in good
agreement with visual observations on the exact temperature of the clear point.
However, since a significant change in sample appearance is required to detect a
change in bulk optical properties, the transmission and reflection automation
schemes often overestimate the onset temperature. This error in the
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determination of the onset point leads to a reduced melting range report, which is
a cause of concern in purity analysis and QC.
Since melt-point determinations have traditionally relied on visual observation of
changes in the physical appearance of solid samples, digital imaging is the most
obvious and natural approach to automation. A useful analogy often used to
illustrate the limitations of the transmission technique describes this methodology
as trying to observe the activity inside a large room by peeking through a
keyhole. The limited field of view provided by such a small aperture naturally
limits your ability to observe everything that goes on and reduces your power to
draw accurate conclusions. At the same time, a high-resolution digital camera,
properly positioned inside the same room, would have no problem observing
everything going on. A different analogy used for the reflection technique
compares the lack of detail in the bulk reflection signals to observing the melts
through wax paper. Important details such as sintering points, sublimation and
the exact onset point can be lost without a detailed view of the subtle changes
that can take place in your samples.
Melt Graphs
In order to provide a real-time display of the changes observed by the digital
camera, a graphical representation of the "melting process vs. temperature" can
be plotted during the analysis.
The plots (referred to as Melt Graphs in the OptiMelt documentation) are a
simplistic representation of the melting process calculated by the digital image
processor, and are stored in memory as part of each final Report. Melt Graphs
can be displayed during and after a melt on the front panel of the instrument or
on a computer screen (using the included Melt View software and USB
connection to a host computer). The Digital Image Processor uses the calculated
Melt Graphs to detect onset, meniscus and clear points based on user-
programmable thresholds (i.e. Onset %, Clear % and Single %) stored in the
OptiMelt's internal memory.
Melt Graphs are generic plots, routinely used to review the changes observed by
the camera during a test, and to fine tune automation parameters (i.e. detection
thresholds) to better match visual and automated determinations. Melt Graphs
are also appended to all printed reports for GLP validation of your analysis
results.
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Figure 6. Melt Graphs provide a simplified representation of the
melt process. Thresholds are assigned for the onset, meniscus
and clear points, and are used by the digital image processor for
automated determinations.
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The three most important qualities of Melt Graphs, which hold true for most well-
behaved chemicals, are that their detection thresholds are (1) very deterministic,
(2) ramp independent and (3) shared by samples that behave in similar manner
during a melt. For example, most white samples which melt without
decomposition share similar MeltGraphs and detection thresholds.
Melt Graphs vs. Ramp Rate (Phenacetin)
1
0.1 Celsius/min
0.2 Celsius/min
0.9
0.5 Celsius/min
0.8 1.0 Celsius/min
2 Celsius/min
0.7 5.0 Celsius/min
0.6
0.5
0.4
0.3
0.2
0.1
0
132 133 134 135 136 137 138 139
Temperature (Celsius)
Figure 7. Melt Graph for phenacetin as a function of heating ramp rate.
Melt Playback (MeltView Software)
The sample images captured by OptiMelt's digital camera (which are used by the
Digital Image Processor to determine unattended melting points and ranges) are
also available for real-time transfer to a PC over the USB interface.
The MeltView software package provided with your OptiMelt can handle real-time
image transfer, allowing you to display and store high-resolution digital images of
the samples (including relevant information such as temperature, time and date)
on your computer screen during analysis. All sample images transferred to the
PC are bundled together as a single package and automatically stored in the
computer's hard disk when the test is completed. This option provides the most
powerful and definitive documentation infrastructure available from any
Stanford Research Systems Phone: (408) 744-9040
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commercial melting point apparatus. Stored images may be recalled at any time,
and melts can be played back frame-by-frame or as movie, by simply moving a
cursor back and forth with your mouse. Being able to replay a test movie after the
fact is an invaluable tool for GLP documentation, fine tuning of results, and
laboratory demonstrations in educational settings.
The combination of excellent image resolution, high magnification, and the power
to carefully step back and forth through a melt provides dramatically enhanced
accuracy compared to visual determinations. The ability to display the melt so
that several people can view it at once makes the MeltView software package
ideal for teaching and lecturing.
Stanford Research Systems Phone: (408) 744-9040