Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors,...
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Autor*in:	Chaudhuri, Sandeep K. [verfasserIn] Kleppinger, Joshua W. Karadavut, OmerFaruk Nag, Ritwik Panta, Rojina Agostinelli, Forest Sheth, Amit Roy, Utpal N. James, Ralph B. Mandal, Krishna C.

Format:	Artikel
Sprache:	Englisch

Erschienen:	2022

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Journal of materials science / Materials in electronics - Springer US, 1990, 33(2022), 3 vom: Jan., Seite 1452-1463
Übergeordnetes Werk:	volume:33 ; year:2022 ; number:3 ; month:01 ; pages:1452-1463

Links:	Volltext

DOI / URN:	10.1007/s10854-021-07623-6

Katalog-ID:	OLC2077986824

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520			\|a Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored.
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allfields	10.1007/s10854-021-07623-6 doi (DE-627)OLC2077986824 (DE-He213)s10854-021-07623-6-p DE-627 ger DE-627 rakwb eng 600 670 620 VZ Chaudhuri, Sandeep K. verfasserin (orcid)0000-0003-4277-121X aut Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. Kleppinger, Joshua W. aut Karadavut, OmerFaruk aut Nag, Ritwik (orcid)0000-0003-0907-9950 aut Panta, Rojina aut Agostinelli, Forest (orcid)0000-0003-1392-3667 aut Sheth, Amit (orcid)0000-0002-0021-5293 aut Roy, Utpal N. aut James, Ralph B. (orcid)0000-0001-6573-2040 aut Mandal, Krishna C. (orcid)0000-0002-7945-7366 aut Enthalten in Journal of materials science / Materials in electronics Springer US, 1990 33(2022), 3 vom: Jan., Seite 1452-1463 (DE-627)130863289 (DE-600)1030929-9 (DE-576)023106719 0957-4522 nnns volume:33 year:2022 number:3 month:01 pages:1452-1463 https://doi.org/10.1007/s10854-021-07623-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2004 GBV_ILN_2015 AR 33 2022 3 01 1452-1463
spelling	10.1007/s10854-021-07623-6 doi (DE-627)OLC2077986824 (DE-He213)s10854-021-07623-6-p DE-627 ger DE-627 rakwb eng 600 670 620 VZ Chaudhuri, Sandeep K. verfasserin (orcid)0000-0003-4277-121X aut Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. Kleppinger, Joshua W. aut Karadavut, OmerFaruk aut Nag, Ritwik (orcid)0000-0003-0907-9950 aut Panta, Rojina aut Agostinelli, Forest (orcid)0000-0003-1392-3667 aut Sheth, Amit (orcid)0000-0002-0021-5293 aut Roy, Utpal N. aut James, Ralph B. (orcid)0000-0001-6573-2040 aut Mandal, Krishna C. (orcid)0000-0002-7945-7366 aut Enthalten in Journal of materials science / Materials in electronics Springer US, 1990 33(2022), 3 vom: Jan., Seite 1452-1463 (DE-627)130863289 (DE-600)1030929-9 (DE-576)023106719 0957-4522 nnns volume:33 year:2022 number:3 month:01 pages:1452-1463 https://doi.org/10.1007/s10854-021-07623-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2004 GBV_ILN_2015 AR 33 2022 3 01 1452-1463
allfields_unstemmed	10.1007/s10854-021-07623-6 doi (DE-627)OLC2077986824 (DE-He213)s10854-021-07623-6-p DE-627 ger DE-627 rakwb eng 600 670 620 VZ Chaudhuri, Sandeep K. verfasserin (orcid)0000-0003-4277-121X aut Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. Kleppinger, Joshua W. aut Karadavut, OmerFaruk aut Nag, Ritwik (orcid)0000-0003-0907-9950 aut Panta, Rojina aut Agostinelli, Forest (orcid)0000-0003-1392-3667 aut Sheth, Amit (orcid)0000-0002-0021-5293 aut Roy, Utpal N. aut James, Ralph B. (orcid)0000-0001-6573-2040 aut Mandal, Krishna C. (orcid)0000-0002-7945-7366 aut Enthalten in Journal of materials science / Materials in electronics Springer US, 1990 33(2022), 3 vom: Jan., Seite 1452-1463 (DE-627)130863289 (DE-600)1030929-9 (DE-576)023106719 0957-4522 nnns volume:33 year:2022 number:3 month:01 pages:1452-1463 https://doi.org/10.1007/s10854-021-07623-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2004 GBV_ILN_2015 AR 33 2022 3 01 1452-1463
allfieldsGer	10.1007/s10854-021-07623-6 doi (DE-627)OLC2077986824 (DE-He213)s10854-021-07623-6-p DE-627 ger DE-627 rakwb eng 600 670 620 VZ Chaudhuri, Sandeep K. verfasserin (orcid)0000-0003-4277-121X aut Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. Kleppinger, Joshua W. aut Karadavut, OmerFaruk aut Nag, Ritwik (orcid)0000-0003-0907-9950 aut Panta, Rojina aut Agostinelli, Forest (orcid)0000-0003-1392-3667 aut Sheth, Amit (orcid)0000-0002-0021-5293 aut Roy, Utpal N. aut James, Ralph B. (orcid)0000-0001-6573-2040 aut Mandal, Krishna C. (orcid)0000-0002-7945-7366 aut Enthalten in Journal of materials science / Materials in electronics Springer US, 1990 33(2022), 3 vom: Jan., Seite 1452-1463 (DE-627)130863289 (DE-600)1030929-9 (DE-576)023106719 0957-4522 nnns volume:33 year:2022 number:3 month:01 pages:1452-1463 https://doi.org/10.1007/s10854-021-07623-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2004 GBV_ILN_2015 AR 33 2022 3 01 1452-1463
allfieldsSound	10.1007/s10854-021-07623-6 doi (DE-627)OLC2077986824 (DE-He213)s10854-021-07623-6-p DE-627 ger DE-627 rakwb eng 600 670 620 VZ Chaudhuri, Sandeep K. verfasserin (orcid)0000-0003-4277-121X aut Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. Kleppinger, Joshua W. aut Karadavut, OmerFaruk aut Nag, Ritwik (orcid)0000-0003-0907-9950 aut Panta, Rojina aut Agostinelli, Forest (orcid)0000-0003-1392-3667 aut Sheth, Amit (orcid)0000-0002-0021-5293 aut Roy, Utpal N. aut James, Ralph B. (orcid)0000-0001-6573-2040 aut Mandal, Krishna C. (orcid)0000-0002-7945-7366 aut Enthalten in Journal of materials science / Materials in electronics Springer US, 1990 33(2022), 3 vom: Jan., Seite 1452-1463 (DE-627)130863289 (DE-600)1030929-9 (DE-576)023106719 0957-4522 nnns volume:33 year:2022 number:3 month:01 pages:1452-1463 https://doi.org/10.1007/s10854-021-07623-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_2004 GBV_ILN_2015 AR 33 2022 3 01 1452-1463
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author	Chaudhuri, Sandeep K.
spellingShingle	Chaudhuri, Sandeep K. ddc 600 Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
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topic_title	600 670 620 VZ Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
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title	Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
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title_full	Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
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title_sort	synthesis of cdzntese single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
title_auth	Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
abstract	Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract In this article, we report the growth of $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous impetus in improving device and sensor performance by compensating for limitations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real $ Cd_{0.9} $$ Zn_{0.1} $$ Te_{0.97} $$ Se_{0.03} $ detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets containing PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. The effect of detector resolution on the efficiency of the CNN was also explored. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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title_short	Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
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author2	Kleppinger, Joshua W. Karadavut, OmerFaruk Nag, Ritwik Panta, Rojina Agostinelli, Forest Sheth, Amit Roy, Utpal N. James, Ralph B. Mandal, Krishna C.
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