Experiments were conducted in a fluidized bed reactor to study the oxidative fast pyrolysis of biomass. The pressure drops along the bed height were measured, and the yield and composition of gas, liquid, and solid products were analyzed at various O2 concentrations. It was found that with the increase in oxygen concentration, the yield of biogas increased, especially CO2 and CO, while yields of bio-oil and biochar decreased. Under oxidative conditions, the fraction of tar in bio-oil was significantly reduced. A multiscale model was developed in the open-source code MFiX by integrating the reactor model based on the coarse-grained DEM-CFD, the intraparticle model, and the biomass oxidative fast pyrolysis kinetics. The model was verified by comparison with experimental data. As the oxygen concentration increases, the mixing and separation of biomass particles decreases, the residence time increases, the axial distribution moves upward, and the Lacey mixing index decreases. This study established experimental and theoretical foundations for designing and scaling up oxidative fast pyrolysis of biomass in fluidized beds.

Marine picocyanobacteria are abundant photosynthetic organisms of global importance. They coexist in the ocean with cyanophages, viruses that infect cyanobacteria. Cyanophages carry many auxiliary metabolic genes acquired from their hosts that are thought to redirect host metabolism for the phage9s benefit 1-5. One such gene is nblA which is present in multiple cyanophage families 2, 6-9. Under nutrient deprivation the cyanobacterial NblA is responsible for inducing proteolytic degradation of the phycobilisome 10-12, the large cyanobacterial photosynthetic light harvesting complex. This increases the pool of amino acids available for essential tasks 12, serving as a survival mechanism 13. Ectopic expression of different cyanophage nblA genes results in host pigment protein degradation 9,7,6. However, the benefit of the cyanophage-encoded NblA for the cyanophage and the broader impact on the host are unknown. Here, using a recently developed genetic manipulation system for cyanophages 14, we reveal that cyanophage NblA significantly accelerates the cyanophage infection cycle, directs degradation of the host phycobilisome and other photosynthetic proteins and reduces host photosynthetic light harvesting efficiency. Furthermore, metagenomic analysis revealed that cyanophages carrying nblA are widespread in the oceans and compose 35% and 65% of oceanic T7-like cyanophages in the surface and deep photic zones, respectively. Our results show a large benefit of the nblA gene to the cyanophage while exerting a negative effect on the host photosynthetic apparatus and host photosynthesis. These findings suggest that nblA-encoding cyanophages have a global effect on the amount of light harvested by oceanic picocyanobacteria.

Non-photochemical quenching (NPQ) mechanisms are crucial for protecting photosynthesis from photoinhibition in plants, algae, and cyanobacteria, and their modulation is a long-standing goal for improving photosynthesis and crop yields. The current work demonstrates that Chlorella ohadii, a green micro-alga that thrives in the desert under high light intensities that are fatal to many photosynthetic organisms does not perform nor require NPQ to protect photosynthesis under constant high light. Instead of dissipating excess energy, it minimizes its uptake by eliminating the photosynthetic antenna of photosystem II. In addition, it accumulates antioxidants that neutralize harmful reactive oxygen species (ROS) and increases cyclic electron flow around PSI. These NPQ-independent responses proved efficient in preventing ROS accumulation and reducing oxidative damage to proteins in high-light-grown cells.

The increasing deployment of large-scale wind turbines in place of conventional generators is expected to lead to the dominance of asynchronous power sources in future power systems, further accelerating the trend toward grid electrification. As a result, the ability of power sources to support system voltage and frequency is gradually diminishing. Synchronous condensers (SCs), which are synchronous machines operating without prime movers, serve as effective devices for providing both dynamic voltage support and inertia. They can significantly enhance the system’s capacity to maintain voltage and frequency stability. However, most existing studies on the optimization of synchronous condenser configurations tend to focus on only one aspect at a time rather than addressing both simultaneously, limiting the full potential of these devices. Optimizing either the voltage or frequency in isolation often results in suboptimal improvements in the other. Moreover, the simultaneous optimization of both voltage and frequency can lead to non-convergent outcomes, complicating the search for an optimal solution. To address this, the paper proposes a hierarchical optimization strategy for synchronous condenser configuration aimed at enhancing both voltage and frequency stability. First, the connection sites for the synchronous condensers are determined based on short-circuit ratio (SCR) constraints. Next, an outer layer optimization model is developed to minimize the total installed capacity of the condensers while taking into account the SCR and transient overvoltage levels as constraints. Following this, an inner layer optimization model is introduced, incorporating a rate of change in the frequency $f_{RoCoF}$ and maximum frequency deviation $f_{nadir}$ as constraints. The model is solved using the bacterial foraging optimization algorithm (BFOA). Finally, a case study of a power grid with a high proportion of wind power validates the effectiveness of the proposed synchronous condenser configuration strategy. Compared to traditional methods, the total required capacity of synchronous condensers was significantly reduced.

Ensuring the safety and reliability of energy systems is very important in power grid operation. However, inherent intricacies and uncertainties of modern-day power systems, such as the mismatch between signal frequency and equipment response speed and the stochasticity associated with renewable energy and load forecasts, create significant challenges for generation dispatch planning. This study proposes a robust optimization approach with constraints based on signal-devices management and data-driven evidential distribution constraints. The first stage of this approach is to decompose and recombine the net power demand according to the Hilbert-Huang transform and device capability. The signal-device management constraints are then established based on this. The second stage formulates an evidential constraint based on data-driven evidential distribution and chance constraints in a distributionally robust optimization framework that caters to the stochasticity associated with renewable energy and load forecasts. The proposed model is validated on a virtual power plant to assess the approach’s efficacy for different dispatching scenarios. Penalty-based sensitivity analysis provides further insights into the proposed method’s performance for varying levels of CO₂ emissions. Simulation results demonstrate that system flexibility becomes increasingly crucial for maintaining system stability and security as the penetration of renewable energy grows. Compared with chance constraint, the proposed data-driven evidential constraint effectively enables the optimization framework to handle stochasticity after sacrificing 0.62% more economic loss and 0.7% more environmental loss. Excessively high penalty parameters for CO₂ do not promote economic development, resulting in 21.08% and 52.51% more economic and environmental losses without obvious environmental protection.

Ammonia is a promising energy storage vector for renewable hydrogen and could become an essential part of the future energy mix. To efficiently utilize ammonia as a fuel, overcome its relatively low reactivity and high tailpipe emissions, and optimize the ratio of different additives, it is crucial to develop accurate chemical kinetic predictive abilities for this system. In this study, we review and compare thermo-kinetic parameters from 19 chemical kinetic reaction mechanisms published in the five-year period of 2018–2023. This comparison reveals a concerning inconsistency in the thermodynamic parameters used for many species in the H/N and H/N/O subsets. One species was even double-counted (having two distinct labels with two similar sets of reactions) in six of these recent mechanisms. Twelve reactions were identified for which the reported rate coefficient deviates by 3 orders of magnitude or more at 1000 K among the reviewed sources. Not all parameters in the literature mechanisms are trackable and properly cited. Ammonia modeling suffers from the “many-model” problem, with significant inconsistency in the parameter values used by different studies. The present work highlights some of these concerns and attempts to advance the current state of NH₃ oxidation modeling by suggesting a coherent set of parameters justified by other work in the literature or by quantum chemistry calculations reported here. It is recommended that future studies of ammonia modeling will properly justify the thermokinetic parameters they use─especially deviations from established values─make all parameter sources trackable, and provide a glossary of chemical structures to all species in the suggested reaction mechanism.

Electrode stability critically impacts energy storage device performance, and despite theoretical recognition, directly observing the evolving electrode–electrolyte interface during the charge–discharge cycle of a supercapacitor remains challenging. This work directly addresses the technical gap by employing liquid-cell-assisted in situ transmission electron microscopy. This innovative approach realizes real-time observation of the dynamic response of the activated carbon electrode during an electrical cycling of the supercapacitor cells. Driving the cell potential above a certain threshold potential during cycling leads to gas evolution that initiates a cascade of events, causing the active carbon to disintegrate. This is being manifested in the electrode’s mass loss and rapid capacity decline upon further cycling. A nanolayer oxide coating of the activated carbon electrode using atomic layer deposition effectively suppresses electrode–electrolyte reactions, stabilizing the electrode and improving the electrochemical properties. The first in situ transmission electron microscopy liquid cell design using an activated carbon substrate represents a breakthrough in understanding supercapacitor degradation, offering a working strategy for electrode dynamics investigation of various energy storage devices.

Membrane separation is in the spotlight as one of the most cost-effective technologies without chemicals for carbon capture. This work aims to fabricate thin film composite (TFC) membranes for boosting CO2 separation. Commercial polysulfone (PSf) flat sheet membranes pre-treated with ethanol solutions were used as support materials by coating with amino-functionalized cross-linked polydimethylsiloxane (PDMS) interlayer. It is worth noting that the developed amino-functionalized PDMS interlayer based on a novel wet-coating method to avoid the penetration of coating solution, which provides better compatibility with the coated polyamide selective layer and also the facilitated transport for CO2 permeation. A thin CO2-selective layer was obtained by interfacial polymerization (IP) between trimesoyl chloride (TMC) in the organic phase and diethylene glycol bis(3-aminopropyl) ether (DGBAmE or EO3) in the aqueous phase to enhance the CO2/N2 selectivity. Both the IP process and the membrane preparation parameters such as heat-treatment temperature, and monomer concentration were systematically optimized. It was found that the best membrane prepared with 9.9 mmol/L TMC monomer solution presents a CO2 permeance of 81 GPU and a CO2/N2 selectivity of 65, which was significantly enhanced from the non-selective supports. This work provides a facile approach for tuning the TFC membrane performance for CO2 separation and can be extended to make high-performance membranes for industrial CO2 capture by selecting more permeable supports.

In fuel cells and electrolyzers, suboptimal proton conductivity and its dramatic drop at low humidity remain major drawbacks in proton exchange membranes (PEMs), including current benchmark Nafion. Sustained through-plane (TP) alignment of nanochannels was proposed as a remedy but proved challenging. We report an anisotropic composite PEM, mimicking the water-conductive composite structure of bamboo that meets this challenge. Micro- and nanoscale alignment of conductive pathways is achieved by in-plane thermal compression of a mat composed of co-electrospun Nafion and poly(vinylidene fluoride) (PVDF) nanofibers stabilizing the alignment. This translates to pronounced TP-enhanced proton conductivity, twice that of pure Nafion at high humidity, 13 times larger at low humidity, and 10 times larger water diffusivity. This remarkable improvement is elucidated by molecular dynamics simulations, which indicate that stronger nanochannels alignment upon dehydration compensates for reduced water content. The presented approach paves the way to overcoming the major drawbacks of ionomers and advancing the development of next-generation membranes for energy applications.

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage.