Biphasic pretreatment excels over conventional sulfuric acid in pinewood biorefinery: An environmental analysis.

This study assesses the environmental impact of pine chip-based biorefinery processes, focusing on bioethanol, xylonic acid, and lignin production. A cradle-to-gate Life Cycle Assessment (LCA) is employed, comparing a novel biphasic pretreatment method (p-toluenesulfonic acid (TsOH)/pentanol, Sc-1) with conventional sulfuric acid pretreatment (H2SO4, Sc-2). The analysis spans biomass handling, pretreatment, enzymatic hydrolysis, yeast fermentation, and distillation. Sc-1 yielded an environmental impact of 1.45E+01 kPt, predominantly affecting human health (96.55%), followed by ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed 32.61%, 29.28%, and 38.11% to the total environmental burdens, respectively. Sc-2 resulted in an environmental burden of 1.64E+01 kPt, with a primary impact on human health (96.56%) and smaller roles for ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed differently at 22.59%, 12.5%, and 64.91%, respectively. Electricity generation was predominant in both scenarios, accounting for 99.05% of the environmental impact, primarily driven by its extensive usage in biomass handling and pretreatment processes. Sc-1 demonstrated a 13.05% lower environmental impact than Sc-2 due to decreased electricity consumption and increased bioethanol and xylonic acid outputs. This study highlights the pivotal role of pretreatment methods in wood-based biorefineries and underscores the urgency of sustainable alternatives like TsOH/pentanol. Additionally, adopting greener electricity generation, advanced technologies, and process optimization are crucial for reducing the environmental footprint of waste-based biorefineries while preserving valuable bioproduct production.

Enhancing docosahexaenoic acid production in Aurantiochytrium species using atmospheric and room temperature plasma mutagenesis and comprehensive multi-omics analysis.

Aurantiochytrium sp. belongs to marine heterotrophic single-cell protist, which is an important decomposer in marine ecosystem. Aurantiochytrium sp. has gained notoriety because of its ability to accumulate high-value docosahexaenoic acid (DHA), but the key factors of DHA synthesis were unclear at present. In this study, Atmospheric and Room Temperature Plasma technology was applied to the mutagenic breeding of Aurantiochytrium sp., and transcriptomics and proteomics were adopted to analyze the DHA-biosynthesis mechanism. According to the growth and DHA accumulation profiles, the mutant strain Aurantiochytrium sp. R2A35 was selected. The DHA content in total lipids was greatly improved from 49.39 % of the wild strain R2 to 63.69 % of the mutant strain. Moreover, the DHA content in the biomass of Aurantiochytrium sp. R2A35 as 39.72 % was the highest DHA productivity reported so far. The differentially expressed genes distinguished from transcriptome and the TMT-identified differential proteins distinguished from proteome confirmed that the expression of acetyl-CoA carboxylase and ketoacyl reductase was up-regulated by 4.78-fold and 6.95-fold, respectively and the fatty acid synthase was concurrently down-regulated by 2.79-fold, so that more precursor was transported to the polyketide synthase pathway, thereby increasing the DHA yield in Aurantiochytrium sp. R2A35. This research would provide reference for the DHA metabolism process and contribute to the understanding of the decomposer - Aurantiochytrium sp. in marine ecosystems.

Microalgae-bacteria consortium for wastewater treatment and biomass production.

The diversity of microalgae and bacteria allows them to form a complementary consortium for efficient wastewater treatment and nutrient recovery. This review highlights the potential of wastewater-derived microalgal biomass as a renewable feedstock for producing animal feed, biofertilisers, biofuel, and many valuable biochemicals. Data corroborated from this review shows that microalgae and bacteria can thrive in many environments. Microalgae are especially effective at utilising nutrients from the water as they grow. This review also consolidates the current understanding of microalgae characteristics and their interactions with bacteria in a consortium system. Recent studies on the performance of only microalgae and microalgae-bacteria wastewater treatment are compared and discussed to establish a research roadmap for practical implementation of the consortium systems for various wastewaters (domestic, industrial, agro-industrial, and landfill leachate wastewater). In comparison to the pure microalgae system, the consortium system has a higher removal efficiency of up to 15% and shorter treatment time. Additionally, this review addresses a variety of possibilities for biomass application after wastewater treatment.

Pyrolysis of waste oils for the production of biofuels: A critical review.

The application of waste oils as pyrolysis feedstocks to produce high-grade biofuels is receiving extensive attention, which will diversify energy supplies and address environmental challenges caused by waste oils treatment and fossil fuel combustion. Waste oils are the optimal raw materials to produce biofuels due to their high hydrogen and volatile matter content. However, traditional disposal methods such as gasification, transesterification, hydrotreating, solvent extraction, and membrane technology are difficult to achieve satisfactory effects owing to shortcomings like enormous energy demand, long process time, high operational cost, and hazardous material pollution. The usage of clean and safe pyrolysis technology can break through the current predicament. The bio-oil produced by the conventional pyrolysis of waste oils has a high yield and HHV with great potential to replace fossil fuel, but contains a high acid value of about 120 mg KOH/g. Nevertheless, the application of CaO and NaOH can significantly decrease the acid value of bio-oil to close to zero. Additionally, the addition of coexisting bifunctional catalyst, SBA-15@MgO@Zn in particular, can simultaneously reduce the acid value and positively influence the yield and quality of bio-oil. Moreover, co-pyrolysis with plastic waste can effectively save energy and time, and improve bio-oil yield and quality. Consequently, this paper presents a critical and comprehensive review of the production of biofuels using conventional and advanced pyrolysis of waste oils.

Effect of elevated temperature on SARS-CoV-2 viability.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide disruption of global health putting healthcare workers at high risk. To reduce the transmission of SARS-CoV-2, in particular during treating the patients, our team aims to develop an optimized isolation chamber. The present study was conducted to evaluate the role of temperature elevation against SARS-CoV-2 viability, where the information would be used to build the isolation chamber. 0.6 mL of the Indonesian isolate of SARS-CoV-2 strain 20201012747 (approximately 10 13 PFU/mL) was incubated for one hour with a variation of temperatures: 25, 30, 35, 40, 45, 50, 55, 60, and 65°C in digital block heater as well as at room temperature (21-23°C) before used to infect Vero E6 cells. The viability was determined using a plaque assay. Our data found a significant reduction of the viral viability from 10 13 PFU/mL to 10 9 PFU/mL after the room temperature was increase to 40°C. Further elevation revealed that 55°C and above resulted in the total elimination of the viral viability. Increasing the temperature 40°C to reduce the SARS-CoV-2 survival could create mild hyperthermia conditions in a patient which could act as a thermotherapy. In addition, according to our findings, thermal sterilization of the vacant isolation chamber could be conducted by increasing the temperature to 55°C. In conclusion, elevating the temperature of the isolation chamber could be one of the main variables for developing an optimized isolation chamber for COVID-19 patients.

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.