title: Development of extrudable hydrogels based on carboxymethyl cellulose–gelatin complex coacervates

authors: Gharanjig, Hamid

subjects: hydrogel

abstract: This study investigates the 3D extrusion printing of a carboxymethyl cellulose (CMC)–gelatin complex coacervate system. Various CMC–gelatin coacervate hydrogels were prepared and analyzed to achieve this goal. The impact of the CMC–gelatin ratio, pH, and total biopolymer concentration on coacervation formation and rheological properties was evaluated to characterize the printability of the samples. Turbidity results indicated that the molecular interactions between gelatin and CMC biopolymers are significantly pH-dependent, occurring within the range of pH 3.7 to pH 5.6 for the tested compositions. Confocal Laser Scanning Microscopy (CLSM) confirmed the presence of coacervates as spherical particles within the optimal coacervation range. Scanning electron microscopy micrographs supported the CLSM findings, revealing greater porosity within this optimal pH range. Rheological characterization demonstrated that all CMC–gelatin hydrogels exhibited pseudoplastic behavior, with an inverse correlation between increased coacervation and decreased shear viscosity. Additionally, the coacervates displayed lower tackiness compared to gelatin hydrogels, with the maximum tackiness normal force for various CMC–gelatin ratios ranging from 1 to 15 N, notably lower than the 29 N observed for gelatin hydrogels. Mixtures with CMC–gelatin ratios of 1:15 and 1:20 exhibited the best shear recovery behavior, maintaining higher strength after shear load. The maximum strength of the CMC–gelatin coacervate system was found at a biopolymer concentration of 6%. However, lower biopolymer content allowed for consistent extrusion. Importantly, all tested samples were successfully extruded at 22 ± 2 °C, with the 1:15 biopolymer ratio yielding the most consistent printed quality. Our research highlights the promise of the CMC–gelatin coacervate system for 3D printing applications, particularly in areas that demand precise material deposition and adjustable properties.

text: Development of extrudable hydrogels based on carboxymethyl cellulose–gelatin complex coacervates This study investigates the 3D extrusion printing of a carboxymethyl cellulose (CMC)–gelatin complex coacervate system. Various CMC–gelatin coacervate hydrogels were prepared and analyzed to achieve this goal. The impact of the CMC–gelatin ratio, pH, and total biopolymer concentration on coacervation formation and rheological properties was evaluated to characterize the printability of the samples. Turbidity results indicated that the molecular interactions between gelatin and CMC biopolymers are significantly pH-dependent, occurring within the range of pH 3.7 to pH 5.6 for the tested compositions. Confocal Laser Scanning Microscopy (CLSM) confirmed the presence of coacervates as spherical particles within the optimal coacervation range. Scanning electron microscopy micrographs supported the CLSM findings, revealing greater porosity within this optimal pH range. Rheological characterization demonstrated that all CMC–gelatin hydrogels exhibited pseudoplastic behavior, with an inverse correlation between increased coacervation and decreased shear viscosity. Additionally, the coacervates displayed lower tackiness compared to gelatin hydrogels, with the maximum tackiness normal force for various CMC–gelatin ratios ranging from 1 to 15 N, notably lower than the 29 N observed for gelatin hydrogels. Mixtures with CMC–gelatin ratios of 1:15 and 1:20 exhibited the best shear recovery behavior, maintaining higher strength after shear load. The maximum strength of the CMC–gelatin coacervate system was found at a biopolymer concentration of 6%. However, lower biopolymer content allowed for consistent extrusion. Importantly, all tested samples were successfully extruded at 22 ± 2 °C, with the 1:15 biopolymer ratio yielding the most consistent printed quality. Our research highlights the promise of the CMC–gelatin coacervate system for 3D printing applications, particularly in areas that demand precise material deposition and adjustable properties.

year: 2025-01-08

title: Text categorization using compression models

authors: Frank, Eibe

abstract: Summary form only given. Test categorization is the assignment of natural language texts to predefined categories based on their concept. The use of predefined categories implies a "supervised learning" approach to categorization, where already-classified articles which effectively define the categories are used as "training data" to build a model that can be used for classifying new articles that comprise "the data". Typical approaches extract features from articles and use the feature vectors as input to a machine learning scheme that learns how to classify articles. The features are generally words. It has often been observed that compression seems to provide a very promising alternative approach to categorization. The overall compression of an article with respect to different models can be compared to see which one it fits most closely. Such a scheme has several potential advantages: it yields an overall judgement on the document as a whole, rather than discarding information by pre-selecting features it avoids the messy and rather artificial problem of defining word boundaries; it deals uniformly with morphological variants of words; depending on the model (and its order), it can take account of phrasal effects that span word boundaries; it offers a uniform way of dealing with different types of documents for example, arbitrary files in a computer system; it generally minimizes arbitrary decisions that inevitably need to be taken to render any learning scheme practical.

text: Text categorization using compression models Summary form only given. Test categorization is the assignment of natural language texts to predefined categories based on their concept. The use of predefined categories implies a "supervised learning" approach to categorization, where already-classified articles which effectively define the categories are used as "training data" to build a model that can be used for classifying new articles that comprise "the data". Typical approaches extract features from articles and use the feature vectors as input to a machine learning scheme that learns how to classify articles. The features are generally words. It has often been observed that compression seems to provide a very promising alternative approach to categorization. The overall compression of an article with respect to different models can be compared to see which one it fits most closely. Such a scheme has several potential advantages: it yields an overall judgement on the document as a whole, rather than discarding information by pre-selecting features it avoids the messy and rather artificial problem of defining word boundaries; it deals uniformly with morphological variants of words; depending on the model (and its order), it can take account of phrasal effects that span word boundaries; it offers a uniform way of dealing with different types of documents for example, arbitrary files in a computer system; it generally minimizes arbitrary decisions that inevitably need to be taken to render any learning scheme practical.

year: 2000-03

title: Māori & Psychology Research Unit Annual Report 2013

authors: Waitoki, Waikaremoana

abstract: Annual report of the Māori and Psychology Research Unit (MPRU) 2013. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community

text: Māori & Psychology Research Unit Annual Report 2013 Annual report of the Māori and Psychology Research Unit (MPRU) 2013. The unit was established in August of 1997. The unit is designed to provide a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Maori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the Faculty of Arts and Social Sciences, the University, and the wider community

year: 2013