Optimizing process conditions in anaerobic digestion could enhance the utilization of organic matter for renewable energy generation. Thus, initial upset substrates with elevated volatile fatty acids were investigated under agitation and non-agitation conditions for optimal bioreactor performance. There were two continuous agitation scenarios for the liquid-state (40 and 100 rpm) with a non-agitated scenario. Similarly, a non-agitated and 40 rpm scenario for the solid-state. The result indicated that the non-agitated liquid-state reactor had the highest methane yield (193 L/kgVS) and lowest retention (51 days) despite delayed microbial adaptation. Of the prominent microbes, the relative abundance of Firmicutes and Archaea_unclassified negatively correlated with VFA at 100 rpm. Contrarily at 40 rpm, Firmicutes correlated positively with VFA, an indication that Firmicutes could withstand acid production at agitation speed ≤40 rpm suggesting that agitation associated with VFA might reduce microbial diversity in an initial upset liquid-state bioreactor. Thus, upset influent could be utilized for energy generation with a non-agitated liquid-state bioreactor.

Optimization can streamline experimental trials by evaluating the parameters and parameter interactions used as the basis for a more optimal downstream process. This study aims to optimize the microwave-assisted pyrolysis process in producing bio-oil from microalgae using response surface methodology (RSM) complemented by an investigation of the reaction mechanisms. A number of key parameters (microwave power, absorbent-to-microalgae ratio, and pyrolysis time) were fine-tuned using a face-centered central composite design. The result showed that microwave technology in slow pyrolysis could produce the bio-oil from microalgae 12 times shorter than conventional heating and quadratic model with a high precision (R² = 0.9832, R²adj = 0.9616) from RSM optimization in predicting experimental values yielded a peak bio-oil yield of 19.11% under specific conditions: 20 min of pyrolysis time, a 0.19 (w/w) microwave absorber to microalgae ratio, and 583 W power. As the complex biomass, the reaction mechanism in chlorella sp. towards this technology including decarboxylation, decarbonylation, dehydration, cracking, deoxygenation, and esterification was proved in GCMS analysis, revealing the presence of key functional groups such as aliphatic, aromatics, alcohols, nitrogenous compounds, fatty acid methyl esters (FAME) and Polycyclic Aromatics Hydrocarbons (PAHs).

Selenium has high theoretical volumetric capacity of 3253 mAh cm⁻³ and acceptable electronic conductivity of 1 × 10⁻⁵ S m⁻¹, which is considered as a potential alternative to sulfur cathode for all-solid-state rechargeable batteries with high energy density. However, the development of all-solid-state Li-Se batteries (ASSLSBs) are hindered by sluggish kinetics and poor cycling life. In this work, trigonal Se nanocrystallines are homogenously distributed in the interspace and on the surface of MXene layers (denoted as Se@MXene composite) by a novel melt-diffusion method. ASSLSBs based on this Se@MXene composite cathode exhibit large specific capacity of 632 mAh g⁻¹ at 0.05 A g⁻¹, high-rate capability over 4 A g⁻¹, and excellent cycling stability over 300 cycles at 1 A g⁻¹. The ex-situ analytical techniques demonstrate that the excellent electrochemical performance of Se@MXene cathode largely arises from structural stability with the assistance of conductive MXene and reversible redox behavior between Li₂Se and Se during the repeating charge/discharge process. Our study points out the potential of material design of Se cathode based on conducting 2D materials with good electrochemical behavior, which may accelerate the practicability of ASSLSBs.

Nickel (10∼50 mol%) doped calcium ferrite nanoparticles (NPs) are synthesized by the solution combustion method using lemon juice extract as a reducing agent, followed by calcination at 500°C. The calcined samples are characterized with different techniques. The Bragg reflections of Nickel doping confirm the formation of a single orthorhombic calcium ferrite phase. The crystallite size is estimated using both Scherrer's and the W-H plot method. The surface morphology consists of irregular size and shaped agglomerated NPs along with pores and voids. A blueshift and a broad absorption spectrum is observed with an increase in the direct energy band gap. The direct energy band gap estimated from Wood and Tauc's relationship was found to be 2.91∼2.97 eV with an increase in dopant concentration. The magnetic analysis provided values for saturation magnetization (M_s), remanence (M_r), and coercivity (H_c), while dielectric studies demonstrated a dielectric constant of 2.81, 2.14, and 1.67 with increasing dopant concentration. The variation of dielectric properties of the sample as a function of frequency in the range 0.1∼20 MHz has been studied at room temperature. The dielectric properties of CaFe₂O₄: Ni (1∼9 mol%) NPs clearly indicate that there is a more pronounced dispersion at lower frequencies, gradually reaching saturation as the frequency increases. The dielectric loss was found to decrease from 4.62, 3.22, and 2.32 with an increase in Ni²⁺ substitution (10, 30, and 50 mol%) respectively. These results indicate the suitability of these samples for applications in memory devices and high-frequency applications.

When selecting biomass feedstock for sustainable heat and electricity generation, higher heating value (HHV) is an important consideration. Meanwhile, the laboratory procedures of using an adiabatic oxygen bomb calorimeter to determine the HHV are strenuous, costly, and time-consuming. As a result, researchers have turned to artificial intelligence techniques such as artificial neural networks (ANN) to predict HHV using data from proximate analysis. Notwithstanding, this approach has been hampered by different case-specific techniques and methodologies given the heterogeneous nature of biomass materials and intricate ANN structures. This study, therefore, examined and compared the efficacy of six training algorithms comprising thirteen distinct training functions of feedforward backpropagation neural networks to predict the HHV of a variety of biomass materials as a function of the proximate analysis. In creating the networks, the neurons of the hidden layer were iterated from 1 to 20 leading to 260 investigated scenarios. Compared to other training algorithms, the Bayesian Regularization and Levenberg-Marquardt with 15 and 12 hidden neurons respectively, demonstrated superior prediction performances based on the Nash-Sutcliff's efficiencies of 0.9044 and 0.8877, and mean squared errors of 0.002271 and 0.00267. It is envisaged that this study will create an insightful paradigm for a rapid selection of best-performing ANN algorithms for biomass HHV prediction.

Global warming poses one of the most critical challenges of the 21st century, leading to significant environmental damage. The extraction and combustion of fossil fuels release substantial amounts of greenhouse gases, thereby contributing to climate change. In response to this pressing issue, plasma-based conversion of carbon dioxide has emerged as a prominent and widely explored solution. Among the various plasma technologies, microwave plasma setups have garnered considerable attention due to their exceptional ability to decompose carbon dioxide, facilitate the dry reforming of methane and reverse water gas shift. These setups are renowned for their high degree of ionization and generation of non-equilibrium plasma, making them a clean and highly efficient method for treating greenhouse gases. However, researchers often face challenges in selecting the appropriate microwave plasma reactors. Thus, the primary objective of this paper is to provide guidance on microwave plasma setups. It is achieved by illustrating experimental configurations based on the microwave operating mechanism and presenting a classification of microwave plasma sources according to their operating principles. Moreover, specific experimental operations are discussed within the scope of our analysis, offering valuable insights to researchers in this field.

As a common industrial solid waste, fly ash requires proper processing and utilization to alleviate environmental pressure. In contrast to earlier low-value treatment methods for fly ash, such as its use in construction materials, it is more practical to explore the high-value utilization of fly ash, considering its elemental ingredient and morphological characteristics. Herein, this work comprehensively reviews the methods and research progress of extracting and preparing silica, alumina, and zeolite respectively derived from silicon and aluminum elements in fly ash. Specifically, the mechanisms and processes of various methods are elucidated in detail, and the virtues and drawbacks of the production technologies are compared to identify a more economical and environmentally friendly method. Importantly, this work first reviews the utilization of fly ash in energy storage electrode materials. Different synthesis and treatment strategies are thoroughly examined, especially in fully utilizing fly ash as a primary resource, converting it into energy storage materials. Finally, this paper summarizes the opportunities and challenges associated with the high-value utilization of fly ash.