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Dissolution of Magnetite and Hematite in Mixtures of Oxalic and Nitric Acid: Mechanisms and Kinetics. MINERALS 2022. [DOI: 10.3390/min12050560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Dissolution mechanisms and kinetics have a key role in better understanding of processes. In this work, magnetite and hematite powder were dissolved in oxalic and nitric acid mixtures at different temperatures. Higher temperature and higher amounts of oxalic acid in the system accelerated the dissolution kinetics but did not result in higher solubility levels. Oxalic acid had also drawbacks in the process since higher amounts in the system promoted formation of a solid product, humboldtine (Fe(II)C2O4∙2H2O), which, in turn, inhibited the dissolution. This problem may be overcome by adding even a small amount of nitric acid to the system. Kinetic analysis showed, in the variable-rate-controlling step, that two linear fits of the Kabai model described the dissolution better in an oxalic acid and acid mixture of 70/30. Thermodynamic data and special cubic models showed that the nitric acid concentration had a significant role in the solubility, whereas the concentration of oxalic acid had only minor effects on solubility. The results also showed that measuring the oxalate and nitrate concentrations did not provide additional information about the dissolution mechanism itself. The pH, however, might be a tool for following the extent of dissolution, even though it is not a direct indicator of the dissolution mechanism.
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Gong Z, Zhu X, Zhang C. D-optimal design of the additive mixture model with multi-response. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:4737-4748. [PMID: 35430838 DOI: 10.3934/mbe.2022221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper proposes the D-optimal design for the additive mixture model with two-response, which is linear model with no interaction terms. The optimality was validated by using the general equivalence theorem, and the corresponding weights are found under which additive model satisfies D-optimality. In addition, relevant statistics and graphics are given to illustrate our results.
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Affiliation(s)
- Zheng Gong
- School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
| | - Xiaoyuan Zhu
- School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China
| | - Chongqi Zhang
- School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
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Liska GR, Cirillo MÂ, de Menezes FS, Bueno Filho JSDS. Machine learning based on extended generalized linear model applied in mixture experiments. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1697821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Shen S, Kang L, Deng X. Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments. Technometrics 2019. [DOI: 10.1080/00401706.2019.1630010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Sumin Shen
- Department of Statistics, Virginia Tech, Blacksburg, VA
| | - Lulu Kang
- Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL
| | - Xinwei Deng
- Department of Statistics, Virginia Tech, Blacksburg, VA
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Miura K, Shima H, Takebe N, Rhie J, Satoh K, Kakugawa Y, Satoh M, Kinouchi M, Yamamoto K, Hasegawa Y, Kawai M, Kanazawa K, Fujiya T, Unno M, Katakura R. Drug delivery of oral anti-cancer fluoropyrimidine agents. Expert Opin Drug Deliv 2017; 14:1355-1366. [PMID: 28379040 DOI: 10.1080/17425247.2017.1316260] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION Sixty years since its introduction, 5-FU still forms the core of chemotherapy regimens for many types of malignancies. 5-FU is a time-dependent drug but is rapidly degraded in plasma by dihydropyrimidine dehydrogenase (DPD). Although originally developed in an intravenous form, 5-FU oral prodrugs were developed with the goal of improving efficacy and minimizing toxicity as well as to capitalize on the advantages of oral drug administration. The inactive 5-FU prodrug is gradually converted into the active form in the systemic circulation. UFT, S-1, and capecitabine are oral 5-FU prodrugs currently in clinical use. However, the efficacy of 5-FU can be further improved by its combination with DPD inhibitors and biochemical modulators, such as uracil and leucovorin, in addition to modifying administration schedules. Areas covered: We focused on the drug delivery of oral 5-FU prodrugs, their pharmacokinetics, and the development of DPD inhibitors. Since oral 5-FU prodrugs have been formulated into combination drugs, we also discussed the regulatory approval of combination drugs. Expert opinion: Many regimens that include intravenously administered 5-FU can be replaced by oral 5-FU prodrugs. Patients would benefit from development of combination 5-FU oral prodrug formulations and its associated path through the combination drug regulatory approval process.
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Affiliation(s)
- Koh Miura
- a Department of Surgery , Miyagi Cancer Center , Natori , Japan
| | - Hiroshi Shima
- b Division of Cancer Chemotherapy , Miyagi Cancer Center Research Institute , Natori , Japan
| | - Naoko Takebe
- c Division of Cancer Treatment and Diagnosis, Cancer Therapy Evaluation Program, Investigational Drug Branch , National Institutes of Health, National Cancer Institute , Bethesda , MD , USA
| | - Julie Rhie
- d Division of Cancer Treatment and Diagnosis, Cancer Therapy Evaluation Program, Regulatory Affairs Branch , National Institutes of Health, National Cancer Institute , Bethesda , MD , USA
| | - Kennichi Satoh
- e Miyagi Cancer Center Research Institute , Division of Cancer Stem Cell , Natori , Japan
| | - Yoichiro Kakugawa
- f Department of Breast Oncology , Miyagi Cancer Center , Natori , Japan
| | - Masayuki Satoh
- a Department of Surgery , Miyagi Cancer Center , Natori , Japan
| | - Makoto Kinouchi
- a Department of Surgery , Miyagi Cancer Center , Natori , Japan
| | | | | | - Masaaki Kawai
- f Department of Breast Oncology , Miyagi Cancer Center , Natori , Japan
| | | | - Tsuneaki Fujiya
- a Department of Surgery , Miyagi Cancer Center , Natori , Japan
| | - Michiaki Unno
- g Department of Surgery , Tohoku University Graduate School of Medicine , Sendai , Japan
| | - Ryuichi Katakura
- h Department of Neurosurgery , Miyagi Cancer Center , Natori , Japan
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