A Production Method for Cr-51 at IEN's Cyclotron

Bastos, Miguel Α. V.; Britto, J. L. Q. de; Vinagre, Ubirajara M.; Silva, Arthur G. da

doi:10.1524/ract.1990.50.4.189

Cited by 8 publications

(4 citation statements)

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“…[5-8, 41, 54, 55], where overlapping energy regions made it possible. Our calculated 51 Cr yield is in good agreement with the yields of Dmitriev and Bastos [41,55], much higher than the yield of Abe [7], slightly higher than the yield of Nickles [8] and slightly lower than the yield curve of Acerbi [6]. Our 48 V yield is in excellent agreement with the yield curve of Acerbi [6], but Dmitriev and Molin [55] reported much higher yield at 22 MeV than our value.…”

Section: Thick Target Yieldssupporting

confidence: 87%

Activation cross-sections of proton induced reactions on vanadium in the 37–65MeV energy range

Ditrói

Tárkányi

Takács

et al. 2016

Nuclear Instruments and Methods in Physics Research Section B:

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Experimental excitation functions for proton induced reactions on natural vanadium in the 37-65 MeV energy range were measured with the activation method using a stacked foil irradiation technique. By using high resolution gamma spectrometry cross-section data for the production of 51,48 Cr, 48 V, 48,47,46,44m,44g,43 Sc and 43,42 K were determined. Comparisons with the earlier published data are presented and results predicted by different theoretical codes (EMPIRE and TALYS) are included. Thick target yields were calculated from a fit to our experimental excitation curves and compared with the earlier experimental yield data. Depth distribution curves to be used for thin layer activation (TLA) are also presented.

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Section: Thick Target Yieldssupporting

confidence: 87%

Activation cross-sections of proton induced reactions on vanadium in the 37–65MeV energy range

Ditrói

Tárkányi

Takács

et al. 2016

Nuclear Instruments and Methods in Physics Research Section B:

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“…1 are also compared with equivalent TENDL-2015 and TENDL-2017 calculations. Ten datasets were rejected (Tanaka and Furukawa [22] (values too low), Albouy et al [24] (values too high), Hontzeas and Yaffe [28] (energy shift), Chodil et al [33] (values too high), Mehta et al (a) [36] (energy shift above 10 MeV), Stück [40] (data in energy range not included in fit), Kailas et al (b) [41] (energy shift), Michel et al [42] (data in energy range not included in fit), Bastos et al [43] (values too high), and Musthafa et al [47] (values too low)), and the remaining twenty-two sets were used in the statistical fitting procedure. These selected data and their experimental uncertainties are shown in Fig.…”

Section: Parentmentioning

confidence: 99%

Recommended nuclear data for medical radioisotope production: diagnostic gamma emitters

Tárkányi

Ignatyuk

Hermanne

et al. 2018

J Radioanal Nucl Chem

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An extensive series of evaluations have been performed as part of an IAEA coordinated research project to study a set of nuclear reactions that produce the diagnostic gamma-ray emitting radionuclides 51 Cr, 99m Tc, 111 In, 123 I and 201 Tl. Recommended cross-section data in the form of excitation functions have been derived, along with quantifications of their uncertainties. These evaluations involved the compilation of all previously published values and newly measured experimental data, followed by critical assessments and selection of those experimental datasets and accompanying uncertainties judged to be fully valid and statistically consistent for model-independent least-squares fitting by means of Padé approximations. Integral yields as a function of the energy were also calculated on the basis of the recommended cross sections deduced from these various fits. All evaluated numerical results and their corresponding uncertainties are available online at www-nds.iaea.org/medical/gamma_emitters.html and also on the medical portal of the International Atomic Energy Agency/Nuclear Data Section (IAEA-NDS) www-nds.iaea.org/medportal/.

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“…In this sense, it is possible to identify recent studies that deal with the themes, collaboration and innovation, jointly. Among the studies we highlight: Kuhl (2012); Kayser and Schreiber (2013); Tuccori et al (2014); Faccin and Balestrin (2015); Bouncken, Pesch and Kraus (2015); Wegner et al (2016); Baggio and Wegner (2016); Kuhl et al (2016); Bastos and Britto (2017); Kuhl et al (2017); Morales et al (2018); Colet and Mozzato (2018).…”

Section: Incremental In Productmentioning

confidence: 97%

“…The collaboration relevance for innovation is also highlighted in more extensive texts and researches, such as the Oslo Handbook, the OCDE Handbook (2005), the Pesquisa Industrial de Inovação Tecnológica (PINTEC) Handbook, the IBGE Handbook (2016), in Brazil, and the Community Innovation Survey (CIS) Handbook and the Innobarometer Survey Handbook, in Europe. Furthermore, some researches (considering only some of the most recent national surveys) deal with the collaboration for innovation (Kayser & Schreiber, 2013;Tuccori, Luppi, Carvalho & Santos, 2014;Faccin & Balestrin, 2015;Baggio & Wegner, 2016;Bastos & Britto, 2017), with the relationship between collaboration and innovation (Kuhl, 2012;Kuhl, Cunha, Maçaneiro & Cunha, 2016;Kuhl, Amarante & Maçaneiro, 2017), and others emphasize that the collaboration contributes to the innovative capacity (Malachias & Meireles, 2009;Mello et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Influência da colaboração interorganizacional na implementação de inovações

Kühl

Costa

2019

Int. J. Innov.

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Collaboration for innovation is addressed in several texts, but few bring empirical evidence of the influence of collaboration on innovation. Thus, the aim of this research is to identify and analyze the degree of the influence of collaboration on the implementation of innovations in Brazilian industries. The theoretical basis used consisted of texts that approach with innovation in a more consistent way and that specifically address collaboration for innovation. For data collection, a structured questionnaire applied to the Brazilian extractive and processing industries was used. This resulted a sample of 254 valid questionnaires. The questionnaire measured the innovation (5 variables), the sources of collaboration (9 variables) and the motives for collaborating (9 variables). The analyzes were based on the Exploratory Factor Analysis for the Motives and Sources constructs and the Multiple Linear Regression Analysis to verify the influence of Motives and Sources on Innovation. The results indicate that collaboration with subsidiaries or group companies, suppliers, customers and competitors has a significant and higher impact in relation to the five types of innovation evaluated. In the cases of radical and incremental innovation in process and Organizational Innovation, besides the sources already mentioned, one of the motives factors (access to resources) was also significant, indicating their influence on the implementation of innovations. As a theoretical contribution, this study seeks empirical evidence on the impact of collaboration on innovation, which is still in its infancy. As a practical contribution, it provides subsidies for companies and for the definition of public policies.

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A Production Method for Cr-51 at IEN's Cyclotron

Cited by 8 publications

References 0 publications

Activation cross-sections of proton induced reactions on vanadium in the 37–65MeV energy range

Activation cross-sections of proton induced reactions on vanadium in the 37–65MeV energy range

Recommended nuclear data for medical radioisotope production: diagnostic gamma emitters

Influência da colaboração interorganizacional na implementação de inovações

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