32 research outputs found

    Analisis Liberalisme Agama Di Malaysia Menurut Perspektif Akidah Islam

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    Tesis ini mengkaji liberalisme agama di Malaysia daripada sudut akidah Islam untuk melihat kedudukan liberalisme serta memberi penjelasan Islam kepada masyarakat. Kajian yang berdasarkan kualitatif ini mendapat data menerusi dokumen dan temu bual dengan pihak yang mendokong liberalisme agama kemudian data diolah dan disusun berdasarkan metode deskriptif dan metode sejarah. Fokus utama kajian adalah kepada isu berkaitan dengan akidah. Hasil kajian mendapati bahawa fahaman liberal agama bertapak di negara ini dan bergerak menerusi seruan kepada pembaharuan dalam agama dengan dimulakan dengan pentafsiran baru terhadap teks agama. Liberalisme agama mengalami penyelewengan yang berlainan tahap; ada yang sungguh bahaya dan ada pula yang kurang bahaya. Penyelewengan yang sungguh bahaya itu ialah penolakan Nabi Muhammad sebagai utusan Allah. Hasil kajian juga mendapati agama Islam mempunyai identiti yang tetap; bukan segala-galanya dalam Islam berubah menurut zaman. Islam berperanan sebagai guru, maka masyarakat tunduk kepada Islam bukan sebaliknya. Setiap bidang ada ahlinya, maka pembaharuan dalam agama Islam juga ditangani oleh ahlinya

    A Translation Analysis Of English Modal Verbs In The First Phone Call From Heaven Novel Into Telepon Pertama Dari Surga By Julanda Tantani

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    The research paper analysis English modal verb in The First Phone Call from Heaven and its translation. The study aims (1) to clarify the translation variations of English modal verb in the novel The First Phone Call from Heaven and its translation and (2) to describe the accuracy of the translation in the novel. The type of this research is descriptive qualitative research. The object of the study is the novel entitled The First Phone Call from Heaven and its translation. The data of this study is English modal verb found in The First Phone Call from Heaven novel. Data source used in this research are the documentation of the data research paper and the informants. The researcher uses document, and questionnaire, in collecting data. The data are analyzed by using comparing method. Based on the analysis, there are 4 types of English modal verb. They are English modal verb translated into Indonesian modal verb consist of 280 data or 63,6%, 8 data or 1,8% English modal verb translated into adverb, 28 data or 4,1% English modal verb translated into adjective and 124 data or 28,1% deleted or omitted. From 440 English modal verbs there are 93 data or 21,1 % accurate, 344 data or 78,2% less accurate and 3 data or 0,7% not accurate. It can be concluded that the English modal verb translation variations are less accurate

    āļ•āļąāļ§āđāļšāļšāļ—āļēāļ‡āļŠāļ–āļīāļ•āļīāļ‚āļ­āļ‡āļ­āļļāļšāļąāļ•āļīāļāļēāļĢāļ“āđŒāļœāļđāđ‰āđ„āļ”āđ‰āļĢāļąāļšāļœāļĨāļāļĢāļ°āļ—āļšāļˆāļēāļāđ€āļŦāļ•āļļāļāļēāļĢāļ“āđŒāļ„āļ§āļēāļĄāđ„āļĄāđˆāļŠāļ‡āļšāļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĒāđāļ”āļ™āđƒāļ•āđ‰

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    Master of Science (Research Methodology),2023The 19 years prolonged period of conflict situation from 2004 until the current year (2022) in the southern provinces of Thailand including Pattani Yala Narathiwat and parts of Songkhla has been affecting on the daily life of people in the area. The cumulative number of incidents, deaths, injuries, widows, and orphans is going to be large. Therefore, this study aimed to investigate the statistical modelling of the injury-date rate and to visualize the injury-date rate in the Deep South provinces of Thailand. The secondary data between 2004 and 2020 were obtained from the Deep South Coordination Center (DSCC), Prince of Songkla University, Pattani Campus. The study areas were sub-districts in Pattani, Yala, Narathiwat, and four districts of Songkhla. The total number of individual observations was 26,938. After managing the individual data into counted data, there were 8,975 observations. The outcome of this study was the injury-death rates per 100,000 population and determinants were year, sub-district, gender, and age. In descriptive analysis, frequency, percentage, minimum, maximum, mean, median and standard deviation were used. In univariate analysis, the ANOVA test was used to test the association between outcome and each determinant. A log-linear regression model was used to investigate the adjusted mean of the injury-death rate by its factors. The results found that the injury-death rate declined sharply from 2007 to 2020 by 48%. The overall mean of injury-death rate was 64.45 cases per 100,000 population. The young males were more vulnerable than the females. Pattani was a riskier area than other provinces. Half of the sub-districts (53.91%) in Pattani had an injury-death rate above the overall mean. More than 60% of the sub-districts, that showed the injury-death rate higher the overall mean, occurred in rural areas of all provinces, except Songkhla, which had no red area. The information from the current study would be useful to the government or other sections for preparing and planning the readiness of health care, compensation, and economic development in the southernmost provinces of Thailand.1. Tuition Fee Waiver Scholarship from the Faculty of Science and Technology, Prince of Songkla University 2. Thesis support funding from the Graduate school, Prince of Songkla UniversityāļŠāļ–āļēāļ™āļāļēāļĢāļ“āđŒāļ„āļ§āļēāļĄāđ„āļĄāđˆāļŠāļ‡āļšāļ—āļĩāđˆāļĒāļ·āļ”āđ€āļĒāļ·āđ‰āļ­āļĄāļēāļĒāļēāļ§āļ™āļēāļ™āļ–āļķāļ‡ 19 āļ›āļĩ āļ•āļąāđ‰āļ‡āđāļ•āđˆāļ›āļĩ 2547 āļˆāļ™āļ–āļķāļ‡āļ›āļąāļˆāļˆāļļāļšāļąāļ™ (2565) āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĒāđāļ”āļ™āļ āļēāļ„āđƒāļ•āđ‰ āđ„āļ”āđ‰āđāļāđˆ āļ›āļąāļ•āļ•āļēāļ™āļĩ āļĒāļ°āļĨāļē āļ™āļĢāļēāļ˜āļīāļ§āļēāļŠ āđāļĨāļ°āļšāļēāļ‡āļŠāđˆāļ§āļ™āļ‚āļ­āļ‡āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļ‡āļ‚āļĨāļē āļŠāđˆāļ‡āļœāļĨāļāļĢāļ°āļ—āļšāļ•āđˆāļ­āļŠāļĩāļ§āļīāļ•āļ›āļĢāļ°āļˆāļģāļ§āļąāļ™āļ‚āļ­āļ‡āļ›āļĢāļ°āļŠāļēāļŠāļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆ āđƒāļ™āļ‚āļ“āļ°āļ—āļĩāđˆāļ•āļąāļ§āđ€āļĨāļ‚āļ—āļĩāđˆāļŠāļ°āļŠāļĄāļĄāļēāļ•āļąāđ‰āļ‡āđāļ•āđˆāđāļĢāļāđ€āļĢāļīāđˆāļĄ āđ„āļĄāđˆāļ§āđˆāļēāļˆāļģāļ™āļ§āļ™āđ€āļŦāļ•āļļāļāļēāļĢāļ“āđŒ āļāļēāļĢāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ• āļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļš āļŦāļāļīāļ‡āļŦāļĄāđ‰āļēāļĒ āđāļĨāļ°āđ€āļ”āđ‡āļāļāļģāļžāļĢāđ‰āļēāļĄāļĩāļˆāļģāļ™āļ§āļ™āļ„āđˆāļ­āļ™āļ‚āđ‰āļēāļ‡āļĄāļēāļ āļ”āļąāļ‡āļ™āļąāđ‰āļ™ āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āļˆāļķāļ‡āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļ”āđ‰āļ§āļĒāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ—āļēāļ‡āļŠāļ–āļīāļ•āļīāđāļĨāļ°āđ€āļžāļ·āđˆāļ­āļ™āļģāđ€āļŠāļ™āļ­āļ‚āđ‰āļ­āļĄāļđāļĨāļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĒāđāļ”āļ™āļ āļēāļ„āđƒāļ•āđ‰āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒ āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āđƒāļŠāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ—āļļāļ•āļīāļĒāļ āļđāļĄāļīāļŠāđˆāļ§āļ‡āļ›āļĩ āļž.āļĻ. 2547 āļ–āļķāļ‡ āļž.āļĻ. 2563 āļ‹āļķāđˆāļ‡āđ„āļ”āđ‰āļĢāļ§āļšāļĢāļ§āļĄāļ‚āđ‰āļ­āļĄāļđāļĨāļˆāļēāļāļĻāļđāļ™āļĒāđŒāļ›āļĢāļ°āļŠāļēāļ™āļ‡āļēāļ™āļ§āļīāļŠāļēāļāļēāļĢāđƒāļŦāđ‰āļ„āļ§āļēāļĄāļŠāđˆāļ§āļĒāđ€āļŦāļĨāļ·āļ­āļœāļđāđ‰āđ„āļ”āđ‰āļĢāļąāļšāļœāļĨāļāļĢāļ°āļ—āļšāļˆāļēāļāđ€āļŦāļ•āļļāļ„āļ§āļēāļĄāđ„āļĄāđˆāļŠāļ‡āļšāļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĒāđāļ”āļ™āļ āļēāļ„āđƒāļ•āđ‰ (āļĻāļ§āļŠāļ•. āļ›āļąāļ•āļ•āļēāļ™āļĩ) āļĄāļŦāļēāļ§āļīāļ—āļĒāļēāļĨāļąāļĒāļŠāļ‡āļ‚āļĨāļēāļ™āļ„āļĢāļīāļ™āļ—āļĢāđŒ āļ§āļīāļ—āļĒāļēāđ€āļ‚āļ•āļ›āļąāļ•āļ•āļēāļ™āļĩ āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļāļēāļĢāļĻāļķāļāļĐāļē āļ„āļ·āļ­ āļˆāļąāļ‡āļŦāļ§āļąāļ”āļ›āļąāļ•āļ•āļēāļ™āļĩ āļĒāļ°āļĨāļē āļ™āļĢāļēāļ˜āļīāļ§āļēāļŠ āđāļĨāļ°āļŠāļĩāđˆāļ­āļģāđ€āļ āļ­āļ‚āļ­āļ‡āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļ‡āļ‚āļĨāļē āļĄāļĩāļˆāļģāļ™āļ§āļ™āļœāļđāđ‰āđ„āļ”āđ‰āļĢāļąāļšāļšāļēāļ”āđ€āļˆāđ‡āļšāđāļĨāļ°āđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļ—āļąāđ‰āļ‡āļŦāļĄāļ” āļ„āļ·āļ­ 26,938 āļ„āļ™ āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āļ—āļģāļāļēāļĢāļˆāļąāļ”āļāļēāļĢāļ‚āđ‰āļ­āļĄāļđāļĨāđƒāļŦāđ‰āļ­āļĒāļđāđˆāđƒāļ™āļĢāļđāļ›āđāļšāļšāļ‚āļ­āļ‡āļˆāļģāļ™āļ§āļ™āļ™āļąāļš āļ”āļąāļ‡āļ™āļąāđ‰āļ™āļˆāļģāļ™āļ§āļ™āļ„āđˆāļēāļŠāļąāļ‡āđ€āļāļ•āļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒ āļ„āļ·āļ­ 8,975 āļĢāļēāļĒāļāļēāļĢ āļ•āļąāļ§āđāļ›āļĢāļ•āļēāļĄāļ„āļ·āļ­ āļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļ•āđˆāļ­āđāļŠāļ™āļ›āļĢāļ°āļŠāļēāļāļĢ āđāļĨāļ°āļ•āļąāļ§āđāļ›āļĢāļ­āļīāļŠāļĢāļ°āļ„āļ·āļ­ āļ›āļĩ āļ•āļģāļšāļĨ āđ€āļžāļĻ āđāļĨāļ°āļ­āļēāļĒāļļ āļŠāļ–āļīāļ•āļīāļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ‚āđ‰āļ­āļĄāļđāļĨāđ€āļŠāļīāļ‡āļžāļĢāļĢāļ“āļ™āļē āđ„āļ”āđ‰āđāļāđˆ āļ„āļ§āļēāļĄāļ–āļĩāđˆ āļĢāđ‰āļ­āļĒāļĨāļ° āļ„āđˆāļēāļ•āđˆāļģāļŠāļļāļ” āļ„āđˆāļēāļŠāļđāļ‡āļŠāļļāļ” āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒ āļ„āđˆāļēāļĄāļąāļ˜āļĒāļāļēāļ™ āđāļĨāļ°āļŠāđˆāļ§āļ™āđ€āļšāļĩāđˆāļĒāļ‡āđ€āļšāļ™āļĄāļēāļ•āļĢāļāļēāļ™ āļŠāļ–āļīāļ•āļīāļ—āļĩāđˆāđƒāļŠāđ‰āđ€āļ›āļĢāļĩāļĒāļšāđ€āļ—āļĩāļĒāļšāļ„āļ§āļēāļĄāđāļ•āļāļ•āđˆāļēāļ‡āļ‚āļ­āļ‡āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āđāļĨāļ°āļ•āļąāļ§āđāļ›āļĢāļ­āļīāļŠāļĢāļ°āđāļ•āđˆāļĨāļ°āļ•āļąāļ§āļ„āļ·āļ­ āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ„āļ§āļēāļĄāđāļ›āļĢāļ›āļĢāļ§āļ™ (Analysis of variance: ANOVA) āļ•āļąāļ§āđāļšāļšāļ—āļēāļ‡āļŠāļ–āļīāļ•āļīāļ—āļĩāđˆāđƒāļŠāđ‰āļ„āļ·āļ­ āļāļēāļĢāļ–āļ”āļ–āļ­āļĒāđ€āļŠāļīāļ‡āđ€āļŠāđ‰āļ™āđƒāļ™āļĢāļđāļ›āđāļšāļšāļĨāđ‡āļ­āļāļāļēāļĢāļīāļ—āļīāļĄ (Log-linear regression) āļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļžāļšāļ§āđˆāļē āļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļĨāļ”āļĨāļ‡āļ­āļĒāđˆāļēāļ‡āđ€āļŦāđ‡āļ™āđ„āļ”āđ‰āļŠāļąāļ”āđƒāļ™āļŠāđˆāļ§āļ‡āļ›āļĩ 2550 āļ–āļķāļ‡ 2563 āđ‚āļ”āļĒāļĨāļ”āļĨāļ‡āļ–āļķāļ‡āļĢāđ‰āļ­āļĒāļĨāļ° 48 āļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āđ€āļ‰āļĨāļĩāđˆāļĒāđ‚āļ”āļĒāļĢāļ§āļĄāļ­āļĒāļđāđˆāļ—āļĩāđˆ 64.45 āļĢāļēāļĒāļ•āđˆāļ­āđāļŠāļ™āļ›āļĢāļ°āļŠāļēāļāļĢ āđ€āļžāļĻāļŠāļēāļĒāļ§āļąāļĒāļŦāļ™āļļāđˆāļĄāļĄāļĩāļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļĄāļēāļāļāļ§āđˆāļēāļœāļđāđ‰āļŦāļāļīāļ‡ āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ›āļąāļ•āļ•āļēāļ™āļĩāđ€āļ›āđ‡āļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ—āļĩāđˆāļĄāļĩāļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļĄāļēāļāļāļ§āđˆāļēāļˆāļąāļ‡āļŦāļ§āļąāļ”āļ­āļ·āđˆāļ™ āđ† āđ‚āļ”āļĒāļ—āļĩāđˆāļ„āļĢāļķāđˆāļ‡āļŦāļ™āļķāđˆāļ‡āļ‚āļ­āļ‡āļ•āļģāļšāļĨ (āļĢāđ‰āļ­āļĒāļĨāļ° 53.91) āđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āļ›āļąāļ•āļ•āļēāļ™āļĩāļĄāļĩāļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļŠāļđāļ‡āļāļ§āđˆāļēāļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļĢāļ§āļĄ āļ­āļĒāđˆāļēāļ‡āđ„āļĢāļāđ‡āļ•āļēāļĄāļĄāļēāļāļāļ§āđˆāļēāļĢāđ‰āļ­āļĒāļĨāļ° 60 āļ‚āļ­āļ‡āļ•āļģāļšāļĨāđƒāļ™āļ—āļļāļāļˆāļąāļ‡āļŦāļ§āļąāļ”āļĄāļĩāļ­āļąāļ•āļĢāļēāļāļēāļĢāļšāļēāļ”āđ€āļˆāđ‡āļšāđ€āļŠāļĩāļĒāļŠāļĩāļ§āļīāļ•āļŠāļđāļ‡āļāļ§āđˆāļēāļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļĢāļ§āļĄ āļŠāđˆāļ§āļ™āđƒāļŦāļāđˆāđ€āļāļīāļ”āļ‚āļķāđ‰āļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļŠāļ™āļšāļ—āļ‚āļ­āļ‡āļ—āļļāļāļˆāļąāļ‡āļŦāļ§āļąāļ”āļĒāļāđ€āļ§āđ‰āļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļ‡āļ‚āļĨāļē āļ‚āđ‰āļ­āļĄāļđāļĨāļˆāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āļˆāļ°āđ€āļ›āđ‡āļ™āļ›āļĢāļ°āđ‚āļĒāļŠāļ™āđŒāđāļāđˆāļŦāļ™āđˆāļ§āļĒāļ‡āļēāļ™āļĢāļąāļāļŦāļĢāļ·āļ­āļŦāļ™āđˆāļ§āļĒāļ‡āļēāļ™āļ­āļ·āđˆāļ™ āđ† āđƒāļ™āļāļēāļĢāđ€āļ•āļĢāļĩāļĒāļĄāļ„āļ§āļēāļĄāļžāļĢāđ‰āļ­āļĄ āļ§āļēāļ‡āđāļœāļ™ āđāļĨāļ°āļāļģāļŦāļ™āļ”āļ™āđ‚āļĒāļšāļēāļĒāđƒāļ™āļāļēāļĢāļ”āļđāđāļĨāļŠāļļāļ‚āļ āļēāļž āļāļēāļĢāđ€āļĒāļĩāļĒāļ§āļĒāļē āđāļĨāļ°āļāļēāļĢāļžāļąāļ’āļ™āļēāđ€āļĻāļĢāļĐāļāļāļīāļˆāđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļēāļĒāđāļ”āļ™āļ āļēāļ„āđƒāļ•āđ‰āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļ•āđˆāļ­āđ„

    Replacement of Hazardous Chemicals Used in Engineering Plastics with Safe and Renewable Hydrogen-Bond Donor and Acceptor Solvent-Pair Mixtures

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    Mixtures of safe and renewable solvents can replace hazardous solvents presently being used in the manufacture of engineering plastics. In this work, a methodology is proposed for identifying solvent-pair mixtures for preparing polymer precursors, with poly­(amic acid) (PAA) being studied as an example. The methodology uses a chemical safety index, Hansen solubility parameters and Kamlet–Taft solvatochromic parameters of the pure and solvent-pair mixtures to identify hydrogen bond acceptor (HBA)–hydrogen bond donor (HBD) solvent-pair combinations. Ten replacement solvent-pairs for PAA syntheses identified were cyclohexanone–methanol, cyclohexanone–ethanol, cyclopentanone–methanol, cyclopentanone–ethanol, Îģ-butyrolactone–methanol, Îģ-butyrolactone–ethanol, Îģ-butyrolactone–water, Îģ-valerolactone–methanol, Îģ-valerolactone–ethanol, and Îģ-valerolactone–water. Homogeneous PAA solutions could be obtained from HBA–HBD solvent-pair mixtures when their solubility parameters were within 21–29 MPa<sup>0.5</sup> and their Kamlet–Taft solvatochromic parameters were π* (>0.67) and Îē (>0.67) for nonaqueous solutions and π* (>0.68) and Îē (>0.59) for aqueous solutions. Replacement solvent-pairs, Îģ-valerolactone–ethanol, Îģ-valerolactone–water, and Îģ-butyrolactone–water gave homogeneous precursor solutions that were comparable with commercial solutions prepared with <i>N</i>-methyl-2-pyrrolidone. The proposed methodology and reported solvatochromic parameters make it is possible to identify other solvent-pair mixtures and new solvent-pairs for preparing polymer precursor solutions used in engineering plastics

    Methodology for Replacing Dipolar Aprotic Solvents Used in API Processing with Safe Hydrogen-Bond Donor and Acceptor Solvent-Pair Mixtures

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    A methodology is presented that allows hazardous dipolar aprotic solvents used in the pharmaceutical processing industries to be replaced with solvent-pair mixtures that consist of a hydrogen-bond donor (HBD) solvent and a hydrogen-bond acceptor (HBA) solvent. The methodology uses the solubility of the active pharmaceutical ingredient (API) in hazardous solvents to estimate the range of required solubility parameters and Kamlet–Taft parameters for the API and then intersects these ranges with the solubility parameters and Kamlet–Taft parameters of the solvent-pair mixtures to identify favorable solvent pairs and possible working compositions. Solvent pairs are ranked according to GSK safety and health scores. The methodology was applied to 13 APIs, where it was found that nonaqueous mixtures (ethanol–isopropyl acetate, ethanol–ethyl acetate, and ethanol–butyl acetate) and aqueous mixtures (water−Îģ-valerolactone and water–dimethyl sulfoxide) are highly ranked and applicable to many APIs. Solvent pairs were eliminated from consideration due to their inability to simultaneously satisfy Kamlet–Taft acidity, basicity, and polarity parameter constraints. The proposed methodology makes it simple to identify and rank HBD–HBA solvent-pair mixtures for replacement of dipolar aprotic solvents used in the pharmaceutical processing industries
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