The aim of this research was to develop the sustainability competencies of preservice chemistry teachers’ through the use of a project-based learning model. Preservice chemistry teachers were engaged in a summer course program in collaboration with national and international universities. The summer course program was conducted online due to the COVID-19 pandemic. The research involved 26 preservice chemistry teachers from a pedagogical university in Jakarta, Indonesia, which joined with other university students from other universities in Indonesia, America, Thailand, and Malaysia. We used a qualitative methodology. Data were collected through interviews, questionnaires, observations, preservice chemistry teachers’ portfolios, and reflective journals. The data were coded into themes and interpreted to reveal that all students engaged successfully in developing their sustainability perspectives, environmental awareness, project development engagement, communication, and collaboration skills. Meanwhile, the preservice chemistry teachers engaged in developing their project in an online summer course program within the framework of sustainability.

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.

Phragmites australis (PHA) is a grass-type biomass, commonly known as reed grass, which has the potential to be a valuable energy and chemical feedstock due to its high yield (4.5–7 kg biomass m⁻² year⁻¹). It is demonstrated that the physicochemical properties and composition of phragmites can be altered by subjecting the feedstock to a combined acid hydrolysis at various level of acid concentrations and torrefaction pre-treatment processes. In this paper, we conducted fast pyrolysis on pretreated PHA, resulting in bio-oil with significantly higher selectivity towards levoglucosenone and appreciably reduced amounts of ketones and aldehydes being produced. The experiments demonstrated that 4% H₃PO₄ acid hydrolysis and 220 °C torrefaction combined pretreatments prior to fast pyrolysis resulted in 17 times increase of relative selectivity to levoglucosenone in the bio-oil portion along with a reduction of ketones and aldehydes relative concentrations from 23% to 13%. Pyrolysis of pretreated PHA produced higher amount of biochar. The phosphorus-enriched biochar offers a potential usage for soil amendment or sorbent material. This study presents an opportunity to convert this underutilized feedstock into valuable bio-based products. Additional in-depth investigation is essential to gather more data for assessing the economic and sustainability features of the proposed process.