• Energy Research
• 2016-2025close
• 7. Clean energy close
• 12. Responsible consumption close
• University of North Texas close

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.

The processing of graphene coated NiO–Ni anode using one CVD system delivered high Li-ion battery performance.

Abstract Commercially available lightweight-flexible CIGS solar cells are investigated under the Low-Intensity-Low-Temperature (LILT) conditions that exist at Mars, Jupiter, and Saturn. Current density-voltage measurements, concentrated solar, and external quantum efficiency measurements are performed under varying temperatures and illumination intensities to determine the applicability and performance of flexible CIGS in outer planetary conditions. The well-known metastability of the CIGS absorber is observed as a result of a barrier to minority carrier extraction at the CIGS/CdS interface under higher intensity illumination. However, despite the low temperatures and low intensities experienced in deeper space, the presence of this barrier does not significantly affect the performance of the solar cells under LILT conditions. This is attributed to the lower photogeneration rate of carriers particularly at conditions relative to Saturn and Jupiter, which appears to be less than the thermionic emission rate across the barrier and therefore the carrier extraction is relatively unaffected under these illumination conditions. At elevated temperatures and/or intensities such as at AM0 and conditions relative to Mars, however, the higher carrier generation rate results in the appearance of large series resistance and a significant loss of fill factor at irradiation levels greater than 1-sun AM0. Proton irradiation of the solar cells systematically reduces the performance, predominately through the formation of defect states in the absorber layer, the presence of which is increasingly more prohibitive in LILT conditions due to the low thermal energy of the minority carriers and the subsequent increased effect of SRH recombination.

Lower-cost alternatives to platinum electrocatalysts are being explored for the sustainable production of hydrogen, but often trial-and-error approaches are used for their development. Now, principles are elucidated that suggest pathways to rationally design efficient metal-free electrocatalysts based on doped graphene.

Machine to Machine (M2M) is a term used to describe the technologies that enable computers, embedded processors, smart sensors, actuators and mobile devices to communicate with one another, take measurements and make decisions--often without human intervention. M2M technology was applied to five commercial buildings in a test. The goal was to reduce electric demand when a remote price signal rose above a predetermine price. In this system, a variable price signal was generated from a single source on the Internet and distributed using the meta-language, XML (Extensible Markup Language). Each of five commercial building sites monitored the common price signal and automatically shed site-specific electric loads when the price increased above predetermined thresholds. Other than price signal scheduling, which was set up in advance by the project researchers, the system was designed to operate without human intervention during the two-week test period. Although the buildings responded to the same price signal, the communication infrastructures used at each building were substantially different. This study provides an overview of the technologies used at each building site, the price generator/server, and each link in between. Network architecture, security, data visualization and site-specific system features are characterized. The results of the test are discussed, including: functionality at each site, measurement and verification techniques, and feedback from energy managers and building operators. Lessons learned from the test and potential implications for widespread rollout are provided.

The energy balance Bowen ratio (EBBR) system produces 30-minute estimates of the vertical fluxes of sensible and latent heat at the local surface. Flux estimates are calculated from observations of net radiation, soil surface heat flux, and the vertical gradients of temperature and relative humidity (RH). Meteorological data collected by the EBBR are used to calculate bulk aerodynamic fluxes, which are used in the Bulk Aerodynamic Technique (BA) EBBR value-added product (VAP) to replace sunrise and sunset spikes in the flux data. A unique aspect of the system is the automatic exchange mechanism (AEM), which helps to reduce errors from instrument offset drift.

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.

Often touted as the most promising next-generation energy storage systems, lithium (Li) metal batteries have drawn extensive interest due to their energy densities beyond those of Li-ion batteries. The use of Li metal, however, presents a major hurdle since it is susceptible to Li dendrite growths, corrosive interfacial reactions, and uncontrolled volume changes. Li-metal protection is an important issue in overcoming those challenges. In particular, studies have shown that molybdenum disulfide (MoS2) can significantly improve the performance and safety of Li metal batteries when used as a protective coating for anodes, separator modification, and stable interfacial layer between solid-electrolytes and Li metal. Herein, we review the successful implementation of MoS2 for improved Li metal batteries including those of the liquid-type and the solid-state cells. We also provide opportunities and prospects of MoS2 applications for safe and practical Li metal batteries.

Abstract The potential of long short-term memory network on ultra-short term wind speed forecast attracted attentions of researchers in recent years. Extending a probabilistic long short-term memory network model to provide an uncertainty estimation than to make a point forecast is more valuable in practice. However, due to complex recurrent structure and feedback algorithm, large scale ensemble forecast based on resampling faces great challenges in reality. Instead, a reliable forecast method needs to be devised. Gaussian process regression is a probabilistic regression model based on Gaussian Process prior. It is reasonable to integrate Gaussian process regression with long short-term memory network for probabilistic wind speed forecast to leverage the superior fitting ability of the deep learning methods and to maintain the probability characteristics of Gaussian process regression. Hence, avoid the repeated training and heavy parameter optimization. The method is evaluated for wind speed forecast using the monitoring dataset provided by the National Wind Energy Technology Center. The results indicated that the proposed method improves the point forecast accuracy by up to 17.2%, and improves the interval forecast accuracy by up to 18.5% compared to state-of-the-art models. This study is of great significance for improving the accuracy and reliability of wind speed prediction and the sustainable development of new energy sources.

AbstractAll‐BODIPY‐based (BODIPY=boron‐dipyrromethene) donor–acceptor systems capable of wide‐band absorbance leading to efficient energy transfer in the near‐IR region are reported. A covalently linked 3‐pyrrolyl BODIPY–BODIPY dimer building block bearing an ethynyl group at the meso‐aryl position is synthesized and coupled with three different monomeric BODIPY/pyrrolyl BODIPY building blocks with a bromo/iodo group under Pd0 coupling conditions to obtain three covalently linked 3‐pyrrolyl‐BODIPY‐based donor–acceptor oligomers in 19–29 % yield. The oligomers are characterized in detail by 1D and 2D NMR spectroscopy, high‐resolution mass spectrometry, and optical spectroscopy. Due to the presence of different functionalized BODIPY derivatives in the oligomers, panchromatic light capture (300–725 nm) is witnessed. Fluorescence studies reveal singlet–singlet energy transfer from BODIPY monomer to BODIPY dimer leading to emission in the 700–800 nm range. Theoretical modeling according to the Förster mechanism predicts ultrafast energy transfer due to good spectral overlap of the donor and acceptor entities. Femtosecond transient absorption studies confirm this to be the case and thus show the relevance of the currently developed all‐BODIPY‐based energy‐funneling supramolecular sytems with near‐IR emission to solar‐energy harvesting applications.