Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Inter- and intra-batch variability in heat and mass transfer during the drying phase of lyophilization is well recognized. Heat transfer variability between individual vials in the same batch arise from both different positions in the vial array and from variations in the bottom contour of the vials, both effects contributing roughly equally to variations in the effective heat transfer coefficient of the vials, K. Both effects can be measured in the laboratory, and variations in average K values as a function of vial position in the array for lab and production can be calculated by use of the simple steady-state heat and mass transfer theory. Typically, in the laboratory dryer, vials on the edge of the array, "edge vials," run 2-4°C warmer than "center vials," but differences between laboratory and manufacturing temperatures are modest. The variability in mass transfer can be assigned to major variations in ice nucleation temperature (both intra-batch and inter-batch), including major differences between laboratory and manufacturing. The net effect of all random variations, for each class of vial, can be evaluated by a simple statistical model-propagation of error, which then allows prediction of the distribution in product temperatures and drying times, and therefore prediction of percent of vials dry and percent of vials collapsed and proximity to the edge of failure for a given process. Good agreement between theoretical and experimentally determined maximum temperatures in primary drying and percent collapsed product demonstrates the calculations have useful accuracy.
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Source |
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http://dx.doi.org/10.1208/s12249-018-1155-4 | DOI Listing |
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