• Energy Research
• 2021-2025close
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Abstract Lignin is considered as a renewable and sustainable resource for producing value-added aromatic chemicals and functional carbon materials. Herein, we develop a one-step catalyst-free depolymerization strategy to convert lignin into aryl monomers and carbon nanospheres simultaneously. Importantly, microwave-assisted depolymerization (MAD) in conjunction with dichloromethane (CH2Cl2) vapors is developed. The total mass yield of guaiacols reached the highest amount of 225.1 mg/g at 600 °C, and the highest yields of phenols (49.0 mg/g) and aromatic hydrocarbons (155.1 mg/g) were obtained at 700 °C. Hydrogen radicals and hydrogen chloride (HCl) are in-situ formed from CH2Cl2, significantly decreasing the activation barrier and reforming pyrolysis vapors to promote the formation of aryl monomers. Interestingly, uniform carbon nanospheres with an average size of 140 nm were produced as co-products at 700 °C. The microwave “hot-spots”, allied with the continuous surface erosion and the decrease in surface energy of lignin-derived carbon precursors by CH2Cl2 vapor, can be considered the driving force for the ultimate formation of carbon nanospheres. The CH2Cl2/MAD system produces aryl monomers (26.8 wt% yield) and carbon nanospheres (36.6 wt% yield) at 700 °C. We provide a facile, intriguing and scalable approach to convert lignin to valuable aryl monomers and sustainable carbon materials that can be applied in the chemistry, energy and environmental fields.

Abstract Lignin is considered as a renewable and sustainable resource for producing value-added aromatic chemicals and functional carbon materials. Herein, we develop a one-step catalyst-free depolymerization strategy to convert lignin into aryl monomers and carbon nanospheres simultaneously. Importantly, microwave-assisted depolymerization (MAD) in conjunction with dichloromethane (CH2Cl2) vapors is developed. The total mass yield of guaiacols reached the highest amount of 225.1 mg/g at 600 °C, and the highest yields of phenols (49.0 mg/g) and aromatic hydrocarbons (155.1 mg/g) were obtained at 700 °C. Hydrogen radicals and hydrogen chloride (HCl) are in-situ formed from CH2Cl2, significantly decreasing the activation barrier and reforming pyrolysis vapors to promote the formation of aryl monomers. Interestingly, uniform carbon nanospheres with an average size of 140 nm were produced as co-products at 700 °C. The microwave “hot-spots”, allied with the continuous surface erosion and the decrease in surface energy of lignin-derived carbon precursors by CH2Cl2 vapor, can be considered the driving force for the ultimate formation of carbon nanospheres. The CH2Cl2/MAD system produces aryl monomers (26.8 wt% yield) and carbon nanospheres (36.6 wt% yield) at 700 °C. We provide a facile, intriguing and scalable approach to convert lignin to valuable aryl monomers and sustainable carbon materials that can be applied in the chemistry, energy and environmental fields.

Agriculture Cyber-Physical System (A-CPS) is becoming increasingly important in enhancing crop quality and productivity by utilizing minimum cropland. This paper introduces the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of ACPS. Majority of the crops were infected by microbial diseases in conventional agriculture. Also, the constantly mutating pathogens cannot be known to the knowledge of the farmer, due to which, there arises a demand to develop a disease prediction system. To prevent this, we use a trained Convolutional Neural Network (CNN) model to perform an analysis of the crop image captured by a health maintenance system. The image capturing along with continuous sensing and intelligent automation is performed by the solar sensor node. The sensor node houses a developed soil moisture sensor which has a high longevity compared to its peers. A real time implementation of the proposed system is demonstrated using a solar sensor node with a camera module, a microcontroller and a smartphone application using which a farmer can monitor the field. The prototype was deployed for three months and has achieved a robust performance by remaining rust-free and sustaining the varied weather conditions. An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.

Agriculture Cyber-Physical System (A-CPS) is becoming increasingly important in enhancing crop quality and productivity by utilizing minimum cropland. This paper introduces the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of ACPS. Majority of the crops were infected by microbial diseases in conventional agriculture. Also, the constantly mutating pathogens cannot be known to the knowledge of the farmer, due to which, there arises a demand to develop a disease prediction system. To prevent this, we use a trained Convolutional Neural Network (CNN) model to perform an analysis of the crop image captured by a health maintenance system. The image capturing along with continuous sensing and intelligent automation is performed by the solar sensor node. The sensor node houses a developed soil moisture sensor which has a high longevity compared to its peers. A real time implementation of the proposed system is demonstrated using a solar sensor node with a camera module, a microcontroller and a smartphone application using which a farmer can monitor the field. The prototype was deployed for three months and has achieved a robust performance by remaining rust-free and sustaining the varied weather conditions. An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.

Pregaming, or drinking before going out, is a commonly practiced risky behavior. Drinking motives are well-established predictors of alcohol use and negative alcohol consequences. Given the influence of context on drinking practices, motives specific to pregaming may affect pregaming behaviors and outcomes above and beyond general drinking motives. Thus, we examined how pregaming motives are related to pregaming behaviors and negative alcohol consequences.Using data from two national cross-sectional online studies, the current study included undergraduates who pregamed at least once in the past month (n=10,200, Mage=19.9, women=61%, white=73.6%; 119 U.S. universities). Participants completed assessments of demographics, general drinking motives, pregaming motives, pregaming frequency/consumption, and negative alcohol consequences. Data were analyzed using hierarchical linear models accounting for nesting of participants within sites.When controlling for demographic factors and general drinking motives, interpersonal enhancement motives and intimate pursuit motives were positively associated with pregaming frequency, pregaming consumption, and negative alcohol consequences. Situational control motives were negatively associated with pregaming consumption and negative alcohol consequences. Barriers to consumption motives were negatively associated with pregaming frequency but positively associated with negative alcohol consequences.Students who pregame to make the night more fun or to meet potential dating partners appear to be at particular risk for negative alcohol consequences. Motives may be modifiable, particularly via cognitive/behavioral strategies. Findings suggest that specific motives may be appropriate intervention targets when trying to reduce pregaming behaviors and negative alcohol consequences.