Thus, quantifying the dynamics of the activity of ERK molecules is an important technical challenge. Time-lapse images were acquired with an IX81 fluorescence microscope Fig. In this dataset, there are about cells in each frame, for a total of 64, cells in all frames.
Cell division occurs at 53 sites, and the number of cells gradually increases. Figure 5 shows an example of the cell lineage display. For these cells, the same cells are linked between images to create a trajectory. The cell population imaged by fluorescence microscopy can be recognized with relatively high accuracy by referring to the peak position of the brightness. Because of the large number of cells, it is efficient to apply the link-type tracking function to create most of the trajectories in this dataset first, and then apply the sequential tracking, manual tracking, and editing functions to correct the errors in some of the mis-tracked areas Fig.
After applying the link-type tracking function, the areas of mis-detection or mis-tracking can be identified, based primarily on the cell lineage display. In the cell lineage, tracking errors often appear in the form of broken trajectories or unnatural branching e. By clicking on a lineage, the corresponding image frame and ROI are immediately recalled and displayed, making it easy to visually check for errors.
In addition, if there is an ROI trajectory on the screen that the user wants to examine in detail, clicking on it will highlight all of the linked ROIs and related data and lineages, helping to visually understand the correctness of the link. Figure 7 shows an example of clicking on an ROI ID number on the original image and highlighting it. After applying the link-type tracking to this dataset, the tracking errors that were identified can be summarized into two main categories.
In the case of two cells moving in close contact, the individual cells cannot be divided accurately and are misidentified as a single cell, resulting in no proper linking. Shape change cell swelling occurs during cell division, and over-segmentation recognition of one cell as a plurality of cells occurs, resulting in erroneous branching by judging that cell division has occurred. Test images. Cell lineage display. Conceptual diagram of the tracking process. Depending on the detection accuracy and other factors, it may not always be possible to track accurately, but it is easy to detect erroneous tracking by displaying the cell lineage and checking for breaks and branches in the genealogy.
For example, sequential tracking can be used to add or modify trajectories only for mistracked areas, and accurate tracking results can be obtained efficiently through the combination. A scene during cell division. Clicking on an ROI ID in the original image immediately displays the corresponding cell lineage and a montage of the image before and after the frame in question. Clicking on any position in the cell lineage will display the corresponding image frame, ROI, and montage image.
Clicking on a montage image will generate the same response. For case 1 , we reapply the sequential tracking function to each of the two cells to recreate the trajectory. When applying sequential tracking, the frame display is updated in real time as the tracking progresses, so the user can visually check the correctness of the destination and positional shift as the process proceeds. In situations where tracking is difficult and misalignment occurs, the process is canceled once, the misalignment is corrected using the mouse, and then tracking is restarted from the corrected position.
By repeating this process, tracking can be performed efficiently. Depending on the closeness of the two cells, the sequential search may be difficult and misalignment may occur. In such cases, the manual tracking function can be useful. The tracking can be performed using both automatic and manual tracking as appropriate. To create the shape of the region, an auxiliary function for automatically setting the shape at the same time as creating the ROI can be used.
However, in some cases, the shape differs between before and after the division because of its irregularity, and an appropriate shape is not set; in this case, an arbitrary shape is set by tracing the boundary line of the region using the pen tool function. The link between the mother cell and daughter cell can be reconfigured individually from the pull-down menu of the newly created ROI. If there are many areas that need to be corrected, it is also possible to recreate all of the ROIs erroneously recognized in the whole frame in advance, and then reapply the link-type tracking function to relink the entire frame.
The above process enables accurate tracking results to be obtained efficiently. We measured the recognition and tracking performance immediately after applying the link-type tracking function before correcting the tracking errors. The evaluation index is the one used in the ISBI Cell Tracking Challenge 6 , and is calculated using a publicly available evaluation program. The recognition accuracy SEG , which is affected by the accuracy of the region shape, is based on the Jaccard similarity of the regions of agreement between the correct answer Ground Truth and the recognition result.
The detection accuracy DET and the tracking accuracy TRA is a graph-based method that represents the cell lineage as a directed acyclic graph, and the agreement score is calculated by comparing the graph created as the correct answer with the graph of the tracking result.
The recognition accuracy SEG was 0. The recognition process including the calculation of feature values took 23 s 9. In addition, sequential tracking, which was used to correct the errors in the link-type tracking in this case study, took an average of 17 s If the user do not need the morphological information of the region and only need to detect and track the spot position, the process is simpler because the user do not need to create the region shape.
However, the basic procedure is the same as above. The state of the cell cycle differs from one cell to another; therefore, quantifying the cell cycle at the single-cell level is important for understanding the molecular mechanisms underlying cell cycle. Each image is produced by the stitching together of multiple original images, and the cells differ markedly in size and luminance; some cells have extremely low luminance values that are the same level as the background.
In this case, we followed the same procedure as in Case 1 for recognition and tracking. After applying the link-type tracking function, most of the tracking errors that could be confirmed at the time before the correction were due to the fact that the boundary between two cells was not detected when they were adjacent to each other, and they were misrecognized as a single cell, resulting in a break in the trajectory and incorrect branching.
As in Case 1, we used the cell lineage display to find the mis-tracked area and correct the error. When misrecognition occurs over a long frame interval, it is effective to apply the sequential tracking function to individual cells that fail to be recognized. However, if the proximity is so close that tracking is difficult and misalignment occurs, it can be corrected by switching to manual tracking as appropriate.
We measured the recognition and tracking performance immediately after applying the link-type tracking function before correcting the erroneous tracking using the same index as in Case 1. The recognition accuracy is lower than that of Case 1 because the boundary between adjacent cells is more ambiguous and there is no difference in luminance between them, making it difficult to segment them.
The recognition process including the calculation of feature values took 46 s 4. A cell imaged by a phase contrast microscope is one of the targets difficult to detect by conventional recognition processing because the cytoplasm is generally irregularly shaped and there is no difference in contrast with the background. In this case study, we applied the DL training and recognition functions.
This software is equipped with a pen tool function as an annotation tool, which allows user to freely create mask regions on the image loaded for training image creation. In this study, we used the SilverTruth annotation image provided in the dataset above and converted it to the correct mask image format for the DL training process.
Specific operation procedures for the DL training and recognition function are described below. Step 1: Load the original image as a training image into the software, and create the correct mask area by tracing the boundary line of the tracking target using the pen tool function.
In this case, the SilverTruth image is converted to the correct mask format and used instead. Step 2: On the GUI, specify the destination of the weight file that will be the training result. In addition, if necessary, set hyper-parameters related to DL training e. Step 3: Start training by pressing the button. When the training is completed, a weight file representing the training result is generated at the specified storage destination.
Step 4: When performing DL recognition, the trained weight file is selected from the file dialog, and the DL recognition service program written in the Python language is started. Step 5: Read the image to be recognized on the software, and execute DL recognition processing in cooperation with a DL recognition service program.
After the DL recognition processing, the tracking processing by the link-type tracking function was executed. When the tracking error was confirmed using the cell lineage display, among others, only a few small fragment areas with long migration and unclear boundaries were not detected. The undetected region was added as a new ROI region using the pen tool function, and when the link-type tracking function was applied again, the correct tracking result was obtained.
As a result of measuring the recognition performance and tracking performance immediately after the application of the link-type tracking function before correcting the erroneous tracking using the same indices as in Case 1, the recognition accuracy SEG was 0. The DL recognition process including the calculation of feature values took s 1.
Through Case 1 and Case 2, it was shown that accurate tracking results could be efficiently obtained by combining multiple tracking functions and cell lineage display, among others, incorporated into this software. Many existing software programs do not have sufficient means of checking the validity of the tracking results, and it is always unclear whether the correct data have been obtained after the automatic tracking process.
Especially when there is a large number of tracked objects or these objects are dense, it is often difficult for software to check the correctness of individual trajectories. To overcome these issues, LIM Tracker has a UI that enables interactive operation and incorporates an excellent visualization means that displays data in real time. Even in the cases described above, the entire linked ROI group trajectory can be highlighted with one click of the mouse on the ROI located on the original image, and the correctness can be easily confirmed visually.
Furthermore, it is possible to highlight a feature list, related data on a scatter plot, or a graph showing time-series feature changes based on trajectory information in conjunction with a click of the mouse on the ROI, and to call up and display a montage group of thumbnails image of the corresponding trajectory. Conversely, when an arbitrary coordinate on the time-series graph is clicked with the mouse, a corresponding image frame is called to highlight the ROI and update the montage image display.
Even when the position or size of the ROI is corrected with the mouse, the feature quantity is immediately recalculated, and the related tables, scatter plot, graph, montage, and so on are updated in real time. These features are also incorporated into the cell lineage display, and the corresponding frame and ROI, among others, can be easily retrieved by simply clicking on the lineage as described in the above example.
In addition, among existing software, there are almost no options providing usable and practical correction methods for tracking results. As introduced in the above example, LIM Tracker can perform sequential tracking, manual tracking, or both by limiting to erroneous tracking points found after automatic tracking by the link-type tracking function.
By partially remaking the track, the erroneous points can be effectively corrected without reprocessing the whole, which greatly reduces the labor-intensiveness of the procedure. As another application of the combined approach, for example, when the object to be processed changes along the time axis and there is a frame section that is difficult to recognize on the way, a combination of changing the method for each section and tracking is possible.
In addition, when the number of targets is small, a combination of utilizing manual tracking only in the section where automatic tracking is difficult is also possible, while utilizing sequential tracking in which highly precise processing is mainly possible. Other features include simple and flexible editing of ROIs and trajectories based on direct manipulation with the mouse. For example, if misrecognition is found during the recognition process, it is easy to correct the over- or under-detected part using the mouse and then perform the linking process.
In addition, if a link error is found after the linking process, it is easy to correct it by relinking directly with the mouse. Moreover, it was shown through case 3 that the latest DL recognition algorithm can be easily trained and recognized by the users themselves. In general, the recognition process using machine learning such as deep learning does not always work properly for user-specific datasets by simply using the default process; in such cases, the recognition process itself needs to be trained by the user.
The standard DL training procedure consists of a series of steps, such as first creating a training image by annotation work for the image, then having the DL training algorithm learn the image, and then generating a training result, that is, a weight model file representing the learned network state. Our software enables users to perform the above steps simply and consistently without requiring expert knowledge of deep learning, machine learning, or other fields, and to obtain highly accurate tracking results by easily constructing the recognition process optimized for the desired data by users themselves.
The main challenges regarding the current state of LIM Tracker can be summarized in three points. First, there is a limitation in functional extensibility. Currently, there is a plugin mechanism for the default non-DL recognition and DL recognition processes, and users can incorporate any algorithm from outside the software; however, for the tracking process, users can only use the preloaded algorithms. In addition, the analysis result to be measured in this software can only use pre-defined features, and there is no extension mechanism to allow users to arbitrarily define the features that they want to output.
Second, interoperability with other analytical software is limited. The output of analysis result in this software is limited to CSV text and image files, and there is no general-purpose interface to enable smooth data linkage from this software to existing more specialized analytical software e.
Third, this software prioritizes support for Windows OS, and some of its functions are limited in Linux. We would like to improve the above points in the future. LIM Tracker is equipped with conventional tracking functions consisting of recognition and frame-to-frame link processing, pattern-matching-based sequential search tracking, and manual tracking functions, which can be seamlessly combined as appropriate to improve the efficiency of tracking operations.
In addition, the DL recognition function can also be used to build highly accurate recognition processes for a wide range of objects. Training functions including annotation tools are built-in, and a series of DL operations from training to recognition can be performed by a simple procedure. Moreover, the software enables interactive operation of data display items ROI, tables, graphs, thumbnails, etc.
It also has a simple and flexible mouse-based correction method in the case of mis-tracking. The application of this software is not limited to any particular subject but can support the quantification of a variety of dynamic phenomena in biological processes. By providing this software to the life science community, we hope to contribute to improving the efficiency of data analysis in time-lapse live imaging studies.
Ekpenyong, A. Viscoelastic properties of differentiating blood cells are fate- and function-dependent. Chalut, K. Chromatin decondensation and nuclear softening accompany Nanog downregulation in embryonic stem cells. Pagliara, S. Auxetic nuclei in embryonic stem cells exiting pluripotency. Hodgson, A. A microfluidic device for characterizing nuclear deformations.
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Cell 52 , — Albeck, J. Frequency-modulated pulses of ERK activity transmit quantitative proliferation signals. Cell 49 , — Jaqaman, K. Robust single-particle tracking in live-cell time-lapse sequences. Methods 5 , — Schmidt, U. He, K. Mask r-cnn, in ICCV. Abdulla, W. Bolya, D. IEEE Trans. Pattern Anal. Article Google Scholar. Nishida, E. The MAP kinase cascade is essential for diverse signal transduction pathways.
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This July, Marvel Comics gives its prehistoric Avengers team their long-anticipated solo story in Avengers 1,, B. In addition to revealing never befor eknown details about the Odinson and his actual mother, the Phoenix Force, the summer gets hot with the official kick off of A. And while all of that is exploding, Shang-Chi's solo series relaunches, Peach Momoko's unique take on Marvel continues with her version of Civil War , Ms. All this, plus the long-awaited debut of Marvel's first-ever Predator series finally arrives, along with a celebration of everyone's favorite race of alien hunters with a series of variants gracing the covers of a number of Marvel's titles.
Stephen Gerding has been a part of CBR for over fifteen years, starting as a part-time freelancer in and working his way up to his current role as Senior News Editor. Over the years, he's been involved in the site's multiple Eisner Award wins, wile helping to define its voice and visual look.
He's also seen the site go through multiple changes, including the launch of the Eisner Award-winning Robot 6 blog, expanding from covering comics to all aspects of pop culture, and the biggest evolution when it transformed from Comic Book Resources to CBR. These impurities are very rarely seen in natural diamond but typically occur in CVD synthetic diamond and thus assist in their identification Breeding and Wang, Many well-known defects in diamond are various combinations of nitrogen impurities with vacancies in the lattice such as N3, H2, H3, H4, and NV centers and have been characterized extensively over the past decade see recommended references in the introduction.
Several other PL peaks occur in natural diamond and have been correlated with diamond color, type, or geographic origin, but the composition of the associated lattice defects has not been conclusively identified by scientists. Later researchers ascribed the To understand how luminescence techniques like PL reveal useful information about diamond formation and color origin, we must first discuss the anatomy of a carbon atom and how it interacts with energy to produce luminescence. It is important to remember that in solids, atoms are not isolated and do interact with surrounding atoms.
These interactions broaden the allowed energy states into bands valence and conduction, at the center of the figure. The energy region between these bands, where certain energy states associated with a perfect diamond lattice are forbidden, is known as the band gap. In figure 4, the band gap is expanded. The excitation energies provided by a range of fluorescence lamps and commonly used lasers are shown, as well as the states of typical diamond defects that fall within the band gap.
In addition, the excitation source generally has an energy equal to or greater than the emission energy; in other words, a nm laser 2. In practice, certain defects are also more efficiently excited by particular laser wavelengths. For example, most PL peaks in the — nm range are more intense when activated by a or nm laser than by a nm laser.
Hence, PL spectra are typically acquired using lasers of various wavelengths. A photon is a single quantum particle of visible light and other forms of electromagnetic radiation, while a phonon is a quantum particle of directional vibration for a group of atoms such as a luminescence-exciting defect within the crystal lattice. The zero-phonon line ZPL is the wavelength at which a photon is emitted between energy levels when no phonons—that is, no vibrations—are involved.
The photon energy of the emitted light from an optical center corresponds to the energy released when an excited electron returns to its ground state; there is also a contribution of internal energy loss due to the lattice vibration. The optical center is usually an imperfection of the crystal lattice—from either the presence of an impurity atom or an interruption in the lattice structure. This distortion modifies the vibration characteristics of the atoms in the vicinity of the optical center within the host material.
The energy levels of the valence band and the conduction band shown in figure 4 are simplified representations. Contained within these electronic energy levels are subdivisions based on vibrational energy levels, which are determined by the way the defects can vibrate within the diamond lattice. One might envision, in very simplified terms, that the ground state is the ground floor of a building and the excited state is the first-floor landing figure 5.
The vibrational levels would be steps on a staircase between the two floors. In PL spectroscopy, the ZPL can be seen as a direct jump from the first-floor landing to the ground floor; as its name implies, a ZPL generally does not involve phonons i. In absorption, the electron resting on the ground floor can be excited up to the first-floor landing or to the first or second step above the first-floor landing.
In fluorescence, an electron might be sitting on the first-floor landing and then jump down to either the ground floor or the first or second step above the ground floor. This variability in the starting and ending staircase steps spreads out the energy released by the resulting photons, creating both the broad fluorescence bands observed at room temperature and the sidebands of the ZPLs usually observed at liquid nitrogen temperatures figure 6.
The ZPL is a sharp peak often referred to in PL spectra precisely because its wavelength is exact, specific to a certain defect, and unambiguous; however, its underlying mechanism may not be understood by scientists who use PL spectra daily. The width of the ZPL is determined by the lifetime of the excited state Sauer et al.
When activated, they will create emission at the ZPL wavelength. As temperature is increased, the electronic transitions are dispersed over broader ranges so that the resulting fluorescence is distributed across a wider wavelength range. At room temperature, the ZPL all but disappears in most cases and only the sideband remains.
At room temperature, there is enough thermal energy to excite many phonons, and the probability of zero-phonon transition is much lower again, see figure 6. Comparison with Fluorescence and Phosphorescence. Occasionally, definitions of terms within a scientific context are different from, and perhaps at odds with, their common usage.
Within the field of gemology, the terms fluorescence , phosphorescence , and photoluminescence have evolved from their general scientific definitions and taken on different meanings. Fluorescence and PL are scientifically regarded as similar processes in which fluorescence is a subset of PL with lifetimes less than 10 nanoseconds again, see figure 3.
Gemology draws a different distinction between these two terms. Both fluorescence and PL detect the same features within gemstones, but the experimental output is vastly different. In gemology, fluorescence has traditionally been measured at room temperature, typically with a broadband or multi-band lamp although LED lamps have been a significant improvement; Luo and Breeding, The eye or a low-resolution spectrometer functions as the detector.
Conversely, PL spectra of diamond are collected at liquid nitrogen temperature using laser excitation and a high-resolution spectrometer figure 7. Figure 8 shows how temperature, excitation source, and spectrometer resolution affect the quality of the emission spectra collected from both traditional fluorescence measurement and PL analysis.
A laser used for PL spectroscopy offers several advantages over both standard UV lamps and tunable spectrofluorometer instruments in detecting fluorescence. Spectrofluorometers are able to filter a single excitation wavelength to a narrow range, but the resulting intensity is reduced because of the lower illumination power. The use of lasers addresses both problems by providing intense illumination over a very narrow wavelength range. In combination with sample cooling, laser excitation often reveals smaller PL peaks that might not be visible at room temperature or with other excitation sources e.
Physicists define fluorescence as luminescence with a decay time of 10 nanoseconds or less, while phosphorescence is delayed luminescence with a decay time greater than approximately 10 nanoseconds again, see figure 3. Sophisticated analytical tools can measure these very short decay times their potential usefulness will be discussed below.
Within gemology, fluorescence is colloquially defined as the emission from the gemstone when a UV source is turned on, and phosphorescence is the observed emission after the UV source is turned off. Practically speaking, gemologists can visually detect phosphorescence only when the decay time is about one second or longer.
A few natural diamonds, such as chameleons and type IIb stones, show visible phosphorescence e. Beyond that, the usefulness of phosphorescence is somewhat limited. Fluorescence and phosphorescence techniques both offer simple, inexpensive methods to create additional identifiers of an individual gem i.
For additional information on specific fluorescence and phosphorescence reactions, see Shigley and Breeding and Luo and Breeding Sensitivity of PL. Many nitrogen-containing defects, such as the neutral and negatively charged NV centers ZPLs at and nm, respectively; Zaitsev, , are routinely observed using PL in type II diamonds, which by definition contain negligible nitrogen impurities measurable by infrared absorption.
As mentioned earlier, a major advantage of PL analysis is its high sensitivity to weak emission of light. Even in diamonds with nitrogen impurity concentrations below the 1—5 parts per million detection limits for infrared absorption instruments i. However, nitrogen A and B aggregates in type Ia diamonds do not have characteristic emissions identifiable using PL, and their presence is best detected with FTIR absorption spectroscopy Zaitsev, In practice, FTIR is performed first on a diamond to identify the diamond type.
This diamond type determination allows gemologists to filter out type II diamonds, which are potentially treated or synthetic, from type Ia, which represent the vast majority of natural diamonds Breeding and Shigley, Standard gemological testing could not identify the HPHT-treated diamonds, but the sensitivity of PL allowed the separation of these goods from their natural-color counterparts.
Since then, the evolution of treatments and synthesis techniques has made the use of these complex analytical identification methods and instruments, such as mapping spectrometers and automated gem testing, more widespread in major gemological laboratories. The brown color of the starting material is thought to be caused by clusters of vacant atom positions i.
When the vacancy clusters are broken up at high temperatures, the brown color is removed, leaving a colorless or near-colorless diamond, but also telltale evidence of the treatment process that is detectable by PL. Natural diamond formation takes millions of years.
The elapsed time of natural diamond formation simply cannot be replicated in a laboratory. Most of the PL features that indicate treatment are not discussed publicly, out of concern that treaters will modify their techniques in an attempt to deceive laboratories. A few have been disclosed, however. For most natural type II diamonds, this ratio is inverted. At the treatment conditions required to remove brown coloration, HPHT processing commonly breaks down nitrogen aggregates to create single substitutional nitrogen impurities, which behave as electron donors.
Charge transfer of the newly available electrons causes the PL intensity of the nm defect the negatively charged NV — center to increase relative to the nm center the neutral NV 0 center Chalain et al. Although PL was initially used to determine HPHT treatment in colorless diamonds, it is now often used to detect diamond formation and color origin for both colorless and colored diamonds Wang et al. Combination-Treated Diamonds.
In the years since HPHT treatment was first introduced, diamonds have been subjected to HPHT annealing in combination with earlier treatments such as irradiation and lower-temperature annealing; this has made the identification process even more complex. It has become increasingly important to investigate the presence and absence of a combination of PL features, in addition to data from other spectroscopic and gemological techniques, rather than simply rely on analysis of a single feature or a single technique.
Synthetic Diamonds. Over the past several years, gem-quality synthetic diamonds grown either by chemical vapor deposition CVD or HPHT methods have become increasingly available in the market, but they still comprise a very small percentage of diamonds analyzed by gemological laboratories. Rapid advances in CVD synthesis techniques in the last decade have complicated the gemological separation of these materials.
High-quality PL spectroscopy has proven essential to their proper identification e. PL spectroscopy can discern a CVD origin and determine if any post-growth treatments have been applied. Other Gem Materials. Although diamonds are the focus of this article, PL spectroscopy can be applied to other gem materials. Raman analysis has been a reliable gemstone identification tool for decades, and its instrumentation often proves quite useful in the collection of PL spectra see box A for a description of the differences between these techniques.
For example, the separation of natural from synthetic spinel can be quite difficult in high-clarity gemstones. Yet PL analysis of stones with chromium fluorescence bands can easily distinguish synthetic spinel Kitawaki and Okano, Similar features provide evidence of heat treatment in natural spinel to enhance their color Saeseaw et al.
Bidny et al. While PL uses a single laser and scans the emission wavelengths, PLE holds the emission wavelength fixed and scans the excitation range. PL spectroscopy has also proven useful for some organic gemstones. Combined with Raman spectroscopy, it can separate natural red coral from dyed coral Smith et al. Features in PL spectra are also able to distinguish natural-color cultured pearls from artificially colored ones Wang et al. When combined with gemological techniques, PL shows features that distinguish tortoiseshell from some of its imitations Hainschwang and Leggio, Unfortunately, most gemstones cannot be cooled to liquid nitrogen temperatures to optimize the results from PL spectroscopy.
Diamonds have extremely high thermal conductivity and a low thermal expansion coefficient, which allows them to withstand low temperatures. Other gems are exposed to a much higher risk of fracture if cooled. For example, the thermal conductivity of corundum is at least 65 times lower than that of diamond Read, , and its coefficient of thermal expansion is five times greater Fiquet et al.
In the last decade, the use of PL to determine diamond origin has become commonplace in gemological research laboratories, while PL analysis itself has become more complex. When HPHT treatment was first introduced, a visual evaluation of the presence or absence of particular PL peaks from a single laser, analyzing two or three wavelengths at most, was adequate for identification. As diamond treatments and synthetics have become more sophisticated, standard procedure now requires many more resources.
Lasers across the UV-visible-NIR wavelength range should be used, as different laser wavelengths efficiently activate different ranges of PL features; the authors regularly use six different laser excitation wavelengths , , , , , and nm. Over the next decade, PL spectroscopy in gemology will continue to evolve toward smaller, more portable instruments see Breeding et al.
For the past decade or so, diamond PL analysis has been performed with the sample temperature stable and the laser power constant. In addition to the exploration of PLE applications again, see Bidny et al. For example, NV color centers in diamond appear identical in PL spectra of natural, treated, and synthetic samples.
Does this center appear different for natural or treated stones at temperatures between liquid nitrogen and room temperature or at the even colder temperatures produced by liquid helium? Thermoluminescence TL , the luminescence response as temperature is increased, has not yet been fully explored for gem materials. Researchers have used TL above room temperature in order to distinguish the dose of gamma radiation in treated CVD diamonds.
At their experimental conditions, the response of natural diamonds was too weak for comparison Karczmarska et al. Additionally, novel TL responses have been observed in diamond Nelson and Breeding, and other colored stones e. Investigations of the decay curves of various diamond defects may reveal important differences between natural, treated, and synthetic diamonds that aid in their identification.
Studies, particularly in biological fields, have shown that a great deal of molecular information is contained within the length of this decay time and within the shape of its decay curve. Additional intensity information could demonstrate two decay times, indicating the presence of multiple defects or energy states Lakowicz, Researchers have also shown that the luminescence lifetime of the NV 0 center ZPL at nm in diamond can be shortened by the presence of single substitutional nitrogen Liaugaudas et al.
Database of Large Quantities of Data. Prelas et al. This quantity of measurable defects, combined with the large quantities of diamonds analyzed in a gem research laboratory, has generated vast repositories of spectral data—a specialized resource that cannot easily be duplicated in most academic environments. Thus, gemological laboratories are unique in their ability to ascertain large-scale spectral trends across thousands of diamonds. To take full advantage of the data, automatic software processing is needed to find and analyze spectral peaks and incorporate them into a robust searchable database.
Our work is ongoing, but we can find and characterize peaks based on their presence or absence and other peak characteristics such as height, as shown in figure The large-scale data mining of spectra can reveal new patterns.
For example, the configuration of the defect causing the The availability of bulk PL peak information from a vast supply of spectra opens numerous statistical possibilities for data analysis across thousands of samples, revealing trends and connections that were heretofore unseen. Every gemstone, whether natural, laboratory grown, or color treated in some way, has a story behind its creation, and every customer has a right to know that story through proper identification.
Over the last decade, PL analysis has become one of the most important tools to document these unique stone histories. PL has also proven to be a reliable gemological identification tool, helping laboratories properly disclose the origin and treatment history of diamonds and other gemstones to ensure public trust in the gem and jewelry industry. Bidny A.
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