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Record Nr. |
UNINA9910800162203321 |
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Titolo |
Carbohydrate chemistry : proven synthetic methods / / edited by Pavol Kovac |
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Pubbl/distr/stampa |
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Boca Raton, FL : , : CRC Press, , 2012- <2014-> |
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ISBN |
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1-4987-6018-X |
0-429-10242-9 |
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Descrizione fisica |
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1 online resource (318 p.) |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Carbohydrates -- Synthesis |
Organic Chemistry |
Chemistry |
Physical Sciences & Mathematics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Vol. 2 edited by: Gijsbert van der Marel, Jeroen Codee. |
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Nota di contenuto |
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""Front Cover""; ""Contents""; ""Foreword""; ""Introduction""; ""Editors""; ""Series Editor""; ""Contributors""; ""Chapter 1: Synthesis of Higher-Carbon Sugars Using the Phosphonate Methodology : Part I-Synthesis of Methyl (methyl 2,3,4-Tri-O-benzyl-α-d-glucopyranosid)uronate""; ""Chapter 2: Synthesis of Higher-Carbon Sugars Using the Phosphonate Methodology : Part II-Synthesis of Dimethyl (methyl 2,3,4-Tri-O-benzyl-α-d-gluco-heptopyranos-6-ulos-7-yl)phosphonate and Application for Carbon Chain Elongation"" |
""Chapter 3: Preparation of Methyl, Butyl, Hexyl, and Octyl 2,3,4-Tri-O-acetyl-d-glucopyranuronates Using Microwave Irradiation""""Chapter 4: Metal-Free, Diamine-Mediated, Oxidative Monoamidation of Benzylated Carbohydrates""; ""Chapter 5: Metal-Free Oxidative Lactonization of Carbohydrates Using Molecular Iodine""; ""Chapter 6: Synthesis of Glycosyl Vinyl Sulfones for Bioconjugation""; ""Chapter 7: Synthesis of 5-Deoxy-β-d-galactofuranosides (5-Deoxy-α-l-arabino-hexofuranosides) Starting from d-Galacturonic Acid Using Photoinduced Electron Transfer Deoxygenation"" |
""Chapter 8: Glycal Transformation into 2-Deoxy Glycosides""""Chapter 9: Regioselective Preparation of 4-Deoxy-erythro-hex-4- |
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enopyranoside Enol Ethers through Acetone Elimination""; ""Chapter 10: Stereoselective Reduction Using Sodium Triacetoxyborodeuteride : Synthesis of Methyl 2,3-Di-O-benzyl-α-d-(4-2H)-glucopyranoside""; ""Chapter 11: Selective Anomeric S-Deacetylation Using Aqueous Sodium Methanethiolate""; ""Chapter 12: Glycosylation of Phenolic Acceptors Using Benzoylated Glycosyl Trichloroacetimidate Donors"" |
""Chapter 24: Phenyl 2-O-acetyl-3-O-allyl-4-O-benzyl-1-thio-β-d-glucopyranoside, a Versatile, Orthogonally Protected Building Block"" |
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2. |
Record Nr. |
UNINA9910139468103321 |
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Titolo |
Bioprocess control / / edited by Denis Dochain |
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Pubbl/distr/stampa |
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London, : ISTE |
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Hoboken, NJ, : John Wiley & Sons, 2008 |
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ISBN |
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1-282-16502-X |
9786612165023 |
0-470-61112-X |
0-470-39371-8 |
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Descrizione fisica |
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1 online resource (244 p.) |
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Collana |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Biotechnological process control |
Biotechnological process monitoring |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Bioprocess Control; Contents; Chapter 1. What are the Challenges for the Control of Bioprocesses?; 1.1. Introduction; 1.2. Specific problems of bioprocess control; 1.3. A schematic view of monitoring and control of a bioprocess; 1.4. Modeling and identification of bioprocesses: some key ideas; 1.5. Software sensors: tools for bioprocess monitoring; 1.6. Bioprocess control: basic concepts and advanced control; 1.7. Bioprocess monitoring: the central issue; 1.8. Conclusions; 1.9. Bibliography; Chapter 2. Dynamic Models of Biochemical Processes: Properties of Models; 2.1. Introduction |
2.2. Description of biochemical processes2.2.1. Micro-organisms and |
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their use; 2.2.2. Types of bioreactors; 2.2.3. Three operating modes; 2.3. Mass balance modeling; 2.3.1. Introduction; 2.3.2. Reaction scheme; 2.3.3. Choice of reactions and variables; 2.3.4. Example 1; 2.4. Mass balance models; 2.4.1. Introduction; 2.4.2. Example 2; 2.4.3. Example 3; 2.4.4. Matrix representation; 2.4.4.1. Example 2 (continuation); 2.4.4.2. Example 1 (continuation); 2.4.5. Gaseous ow; 2.4.6. Electroneutrality and affinity constants; 2.4.7. Example 1 (continuation); 2.4.8. Conclusion; 2.5. Kinetics |
2.5.1. Introduction2.5.2. Mathematical constraints; 2.5.2.1. Positivity of variables; 2.5.2.2. Variables necessary for the reaction; 2.5.2.3. Example 1 (continuation); 2.5.2.4. Phenomenological knowledge; 2.5.3. Specific growth rate; 2.5.4. Representation of kinetics by means of a neural network; 2.6. Validation of the model; 2.6.1. Introduction; 2.6.2. Validation of the reaction scheme; 2.6.2.1. Mathematical principle; 2.6.2.2. Example 4; 2.6.3. Qualitative validation of model; 2.6.4. Global validation of the model; 2.7. Properties of the models |
2.7.1. Boundedness and positivity of variables2.7.2. Equilibrium points and local behavior; 2.7.2.1. Introduction; 2.8. Conclusion; 2.9. Bibliography; Chapter 3. Identification of Bioprocess Models; 3.1. Introduction; 3.2. Structural identifiability; 3.2.1. Development in Taylor series; 3.2.2. Generating series; 3.2.3. Examples for the application of the methods of development in series; 3.2.4. Some observations on the methods for testing structural identifiability; 3.3. Practical identifiability; 3.3.1. Theoretical framework; 3.3.2. Confidence interval of the estimated parameters |
3.3.3. Sensitivity functions3.4. Optimum experiment design for parameter estimation (OED/PE); 3.4.1. Introduction; 3.4.2. Theoretical basis for the OED/PE; 3.4.3. Examples; 3.5. Estimation algorithms; 3.5.1. Choice of two datasets; 3.5.2. Elements of parameter estimation: least squares estimation in the linear case; 3.5.3. Overview of the parameter estimation algorithms; 3.6. A case study: identification of parameters for a process modeled for anaerobic digestion; 3.6.1. The model; 3.6.2. Experiment design; 3.6.3. Choice of data for calibration and validation; 3.6.4. Parameter identification |
3.6.5. Analysis of the results |
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Sommario/riassunto |
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Giving an overview of the challenges in the control of bioprocesses, this comprehensive book presents key results in various fields, including: dynamic modeling; dynamic properties of bioprocess models; software sensors designed for the on-line estimation of parameters and state variables; control and supervision of bioprocesses. |
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