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1. |
Record Nr. |
UNINA9910254152103321 |
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Autore |
Bajpai P (Pratima) |
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Titolo |
Carbon Fibre from Lignin / / by Pratima Bajpai |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XIII, 77 p. 10 illus.) |
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Collana |
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SpringerBriefs in Materials, , 2192-1091 |
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Disciplina |
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Soggetti |
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Ceramics |
Glass |
Composite materials |
Forest products |
Polymers |
Ceramics, Glass, Composites, Natural Materials |
Wood Science & Technology |
Polymer Sciences |
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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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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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General Background and Introduction -- Lignin -- Carbon Fibre -- Carbon Fibre Market -- Lignin as a Raw Material for Carbon Fibre -- Industrial Lignin Production -- Production of Carbon Fibre from Lignin -- Lignin fiber Spinning and Conversion to Carbon Fibre -- Future Directions of Carbon Fibre Industry -- Future perspectives. |
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Sommario/riassunto |
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This book presents detailed information on the production and properties of carbon fibers derived from lignin precursors. Focusing on future directions in the carbon fiber industry, it also introduces a novel process for obtaining high-purity lignin, a key aspect in the manufacture of high-quality carbon fiber. Carbon fiber is currently the most preferred lightweight manufacturing material and is rapidly becoming the material of choice for manufacturers around the world. Although more than 80% of commercial carbon fiber is estimated to use PAN (polyacrylonitrile) as a precursor, carbon fiber manufactured from PAN is expensive and therefore its application is limited to high-performance structural materials. Lignin is the second most abundant |
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biopolymer in nature after cellulose and offers a carbon-rich, renewable resource. As a byproduct of the pulp and paper industry and the production of cellulosic ethanol, lignin is also available at low cost, making it an economically attractive alternative to PAN for the production of carbon fibers, as highlighted in this book. The information presented will be of interest to all those involved in the investigation of carbon fiber materials, carbon fiber manufacturers and carbon fiber users. |
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2. |
Record Nr. |
UNINA9910970776403321 |
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Autore |
Loukoianova Elena |
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Titolo |
Banking Crises and Crisis Dating : : Theory and Evidence / / Elena Loukoianova, Gianni De Nicolo, John Boyd |
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Pubbl/distr/stampa |
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Washington, D.C. : , : International Monetary Fund, , 2009 |
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ISBN |
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9786612843556 |
9781462390076 |
1462390072 |
9781452722825 |
145272282X |
9781451872880 |
1451872887 |
9781282843554 |
1282843559 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (52 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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Bank failures - Econometric models |
Banks and banking - Econometric models |
Economic indicators |
Bank credit |
Banking crises |
Banking |
Banks and Banking |
Banks and banking |
Banks |
Credit |
Crisis management |
Currency crises |
Deposit insurance |
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Depository Institutions |
Economic & financial crises & disasters |
Financial Crises |
Financial crises |
Financial Institutions and Services: Government Policy and Regulation |
Financial Risk Management |
Foreign Exchange |
Macroeconomics |
Micro Finance Institutions |
Monetary economics |
Monetary Policy, Central Banking, and the Supply of Money and Credit: General |
Money and Monetary Policy |
Mortgages |
United States |
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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 contenuto |
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Contents; I. Introduction and Summary; II. Major Classifications of Banking Crises; III. BC Indicators an d Their Discrepancies; IV. A Simple Banking Model; V. Evidence from Cross-Country Data: Benchmark Specifications; A. Logit Regressions with BC Indicators as Dependent Variables; B. SBS indicators Predict BC indicators; C. Logit Regressions with SBS Indicators as Dependent Variables; VI. Market Structure and Deposit Insurance; A. Bank Market Structure and Competition; B. Deposit Insurance; VII. Currency and "Twin" Crises; A. BC and SBS Indicators as Dependent Variables |
B. Currency Crises as Dependent VariablesVIII. Evidence from Bank-Level Data; A. Measures of Systemic Bank Shocks; B. SBS indicators Predict BC indicators; C. Market Structure, Deposit Insurance and External Shocks; VI. Conclusion; References; Tables; 1. BC Indicators; 2. Logit Regressions with Start Date BC Indicators (crisis dates after the first crisis year excluded); 3. Logit Regressions with BC Indicators (all observations with crisis dating); 4. Logit Regressions: Do SBS Lending Indicators Predict BC Indicators?; 5. Logit Regressions: Do SBS Deposit Indicators Predict BC Indicators? |
6. Logit Regressions with SBS Indicators ad Dependent Variables7. Logit Regressions: BC Indicators and Bank Concentration Measures; 8. Logit Regressions: SBS Indicators and Bank Concentration Measures; 9. Logit Regressions: BC Indicators, SBS Indicators and Deposit Insurance; 10. Logit Regressions: BC Indicators, SBS Indicators, Deposit Insurance Features and Quality of Institutions; 11. Logit Regressions: BC Indicators, Currency and Twin Crises; 12. Logit Regressions: SBS Indicators, Currency and Twin Crises; 13. Logit Regressions: Currency Crises and SBS Indicators |
14. Bank Level Data, Random Effect Logit Regressions: SBS Indicators Predict BC Indicators15. Bank Level Data, Random Effect Logit Regressions: Determinants of SBS and BC Indicators; A1. ""Systemic"" Banking Crises and Crisis Dating in Different Classifications |
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Sommario/riassunto |
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Many empirical studies of banking crises have employed "banking crisis" (BC) indicators constructedusing primarily information on government actions undertaken in response to bank distress. Weformulate a simple theoretical model of a banking industry which we use to identify and constructtheory-based measures of systemic bank shocks (SBS). Using both country-level and firm-level samples, we show that SBS indicators consistently predict BC indicators based on four major BCseries that have appeared in the literature. Therefore, BC indicatorsactually measure lagged government responses to systemic bank shocks, rather than the occurrence of crises per se. We re-examine the separate impact of macroeconomic factors, bank market structure, deposit insurance, andexternal shocks on the probability of a systemic bank shocks and on the probability of governmentresponses to bank distress. The impact of these variables on the likelihood of a government responseto bank distress is totally different from that on the likelihood of a systemic bank shock.Disentangling the effects of systemic bank shocks and government responses turns out to be crucial inunderstanding the roots of bank fragility. Many findings of a large empirical literature need to be re-assessed and/or re-interpreted. |
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