Supramolecular chemistry enables vat photopolymerization 3D printing of novel water-soluble tablets
KeywordVat photopolymerization additive manufacturing
3D printed drug products and printlets
Printing formulations and drug delivery systems
Clinical translation of printed medicines
Biocompatibility and safety of 3D printed devices
Rights© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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AbstractVat photopolymerization has garnered interest from pharmaceutical researchers for the fabrication of personalised medicines, especially for drugs that require high precision dosing or are heat labile. However, the 3D printed structures created thus far have been insoluble, limiting printable dosage forms to sustained-release systems or drug-eluting medical devices which do not require dissolution of the printed matrix. Resins that produce water-soluble structures will enable more versatile drug release profiles and expand potential applications. To achieve this, instead of employing cross-linking chemistry to fabricate matrices, supramolecular chemistry may be used to impart dynamic interaction between polymer chains. In this study, water-soluble drug-loaded printlets (3D printed tablets) are fabricated via digital light processing (DLP) 3DP for the first time. Six formulations with varying ratios of an electrolyte acrylate …
CitationOng JJ, Chow YL, Gaisford S et al (2023) Supramolecular chemistry enables vat photopolymerization 3D printing of novel water-soluble tablets. International Journal of Pharmaceutics. 643: 123286.
Link to publisher’s versionhttps://doi.org/10.1016/j.ijpharm.2023.123286
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Arabic text recognition of printed manuscripts. Efficient recognition of off-line printed Arabic text using Hidden Markov Models, Bigram Statistical Language Model, and post-processing.Qahwaji, Rami S.R.; Al-Muhtaseb, Husni A. (University of BradfordDigital Imaging, School of Computing, Informatics and Media, 2010-09-01)Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired people and understanding filled forms. This research work aims to contribute to the current research in the field of optical character recognition (OCR) of printed Arabic text by developing novel techniques and schemes to advance the performance of the state of the art Arabic OCR systems. Statistical and analytical analysis for Arabic Text was carried out to estimate the probabilities of occurrences of Arabic character for use with Hidden Markov models (HMM) and other techniques. Since there is no publicly available dataset for printed Arabic text for recognition purposes it was decided to create one. In addition, a minimal Arabic script is proposed. The proposed script contains all basic shapes of Arabic letters. The script provides efficient representation for Arabic text in terms of effort and time. Based on the success of using HMM for speech and text recognition, the use of HMM for the automatic recognition of Arabic text was investigated. The HMM technique adapts to noise and font variations and does not require word or character segmentation of Arabic line images. In the feature extraction phase, experiments were conducted with a number of different features to investigate their suitability for HMM. Finally, a novel set of features, which resulted in high recognition rates for different fonts, was selected. The developed techniques do not need word or character segmentation before the classification phase as segmentation is a byproduct of recognition. This seems to be the most advantageous feature of using HMM for Arabic text as segmentation tends to produce errors which are usually propagated to the classification phase. Eight different Arabic fonts were used in the classification phase. The recognition rates were in the range from 98% to 99.9% depending on the used fonts. As far as we know, these are new results in their context. Moreover, the proposed technique could be used for other languages. A proof-of-concept experiment was conducted on English characters with a recognition rate of 98.9% using the same HMM setup. The same techniques where conducted on Bangla characters with a recognition rate above 95%. Moreover, the recognition of printed Arabic text with multi-fonts was also conducted using the same technique. Fonts were categorized into different groups. New high recognition results were achieved. To enhance the recognition rate further, a post-processing module was developed to correct the OCR output through character level post-processing and word level post-processing. The use of this module increased the accuracy of the recognition rate by more than 1%.
Model and design of small compact dielectric resonator and printed antennas for wireless communications applications. Model and simulation of dialectric resonator (DR) and printed antennas for wireless applications; investigations of dual band and wideband responses including antenna radiation performance and antenna design optimization using parametric studiesAbd-Alhameed, Raed; McEwen, N.J.; Mujtaba, Iqbal M.; Elmegri, Fauzi (University of BradfordFaculty of Engineering and Informatics, 2015)Dielectric resonator antenna (DRA) technologies are applicable to a wide variety of mobile wireless communication systems. The principal energy loss mechanism for this type of antenna is the dielectric loss, and then using modern ceramic materials, this may be very low. These antennas are typically of small size, with a high radiation efficiency, often above 95%; they deliver wide bandwidths, and possess a high power handling capability. The principal objectives of this thesis are to investigate and design DRA for low profile personal and nomadic communications applications for a wide variety of spectrum requirements: including DCS, PCS, UMTS, WLAN, UWB applications. X-band and part of Ku band applications are also considered. General and specific techniques for bandwidth expansion, diversity performance and balanced operation have been investigated through detailed simulation models, and physical prototyping. The first major design to be realized is a new broadband DRA operating from 1.15GHz to 6GHz, which has the potential to cover most of the existing mobile service bands. This antenna design employs a printed crescent shaped monopole, and a defected cylindrical DRA. The broad impedance bandwidth of this antenna is achieved by loading the crescent shaped radiator of the monopole with a ceramic material with a permittivity of 81. The antenna volume is 57.0 37.5 5.8 mm3, which in conjunction with the general performance parameters makes this antenna a potential candidate for mobile handset applications. The next class of antenna to be discussed is a novel offset slot-fed broadband DRA assembly. The optimised structure consists of two asymmetrically located cylindrical DRA, with a rectangular slot feed mechanism. Initially, designed for the frequency range from 9GHz to 12GHz, it was found that further spectral improvements were possible, leading to coverage from 8.5GHz to 17GHz. Finally, a new low cost dual-segmented S-slot coupled dielectric resonator antenna design is proposed for wideband applications in the X-band region, covering 7.66GHz to 11.2GHz bandwidth. The effective antenna volume is 30.0 x 25.0 x 0.8 mm3. The DR segments may be located on the same side, or on opposite sides, of the substrate. The end of these configurations results in an improved diversity performance.
Recognition of off-line printed Arabic text using Hidden Markov Models.Al-Muhtaseb, Husni A.; Mahmoud, Sabri A.; Qahwaji, Rami S.R. (Elsevier, 27/06/2008)This paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256). Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts. The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages.