Voltage Stability Research Articles

A Shared Anode Flow Field for Direct Methanol Fuel Cell with Enhanced Performance and Decreased Volume

Low‐volume power density remains a significant barrier to the portable application of direct methanol fuel cell (DMFC). Herein, a shared anode flow field (SAFF) structure is introduced in an active DMFC to reduce stack volume and improve discharge performance. The differences in discharge performance between the bi‐cell with SAFF and the bi‐cell with traditional anode flow field (TAFF), coupled with the effect of operating conditions on performance, are investigated by polarization curve, electrochemical impedance spectra, and voltage versus time curves. The results show that the SAFF structure enhances anode mass transfer, resulting in an improvement in peak power density and voltage stability of the bi‐cell compared to the TAFF structure. In addition, the bi‐cell with SAFF achieves its highest peak power density at a lower methanol concentration, alleviating the methanol crossover caused by high concentration. The SAFF structure is an attractive choice for DMFC portable applications.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Security-constrained unit commitment model considering frequency and voltage stabilities with multiresource participation

The rapidly increasing proportion of renewable energy sources has led to a reduction in the relative share of synchronous units, which has resulted in a decline in the inertia of the power system and a decrease in its voltage support capacity. This has led to several issues related to the frequency and voltage stabilities of the power system. To ensure these frequency and voltage stabilities, it is necessary to maintain a number of synchronous units online in the day-ahead generation schedule. First, the dynamic frequency change process after a power system fault is discussed, and a linear expression is derived for the frequency stability constraints involving energy storage systems and wind turbines. Second, the short-circuit capacity of the bus is characterized to describe the strength of voltage support, and the minimum short-circuit capacity requirement of the bus is solved based on the transient voltage recovery problem after clearance of the short-circuit faults. The total short-circuit capacity provided by the unit to the bus is then calculated through the network reactance matrix to establish the voltage stability constraint. Subsequently, the security-constrained unit commitment model considering frequency and voltage stabilities (FVS-SCUC) is established. Finally, the effectiveness of the proposed model is demonstrated through a numerical simulation of the IEEE-39 system comprising storage and wind turbines. The model ensures the frequency and voltage securities of the system, and the renewable energy output is improved upon considering energy storage. Thus, the overall system cost was reduced by nearly 30% by considering the frequency regulation effects of the energy storage system and wind turbine as well as the voltage regulation effects of the energy storage system.

Experimental study on the performance of proton exchange membrane fuel cell with foam/channel composite flow fields

Metal foam flow field (MF-FF) has shown great potential in enhancing the proton exchange membrane fuel cell (PEMFC) performance, while the deficiency of lacking mainstream directions still merits improvement. In this work, we proposed a foam/channel composite flow field (FCC-FF), which combines the advantages of enhanced transport of the MF-FF and clear mainstream direction of the parallel serpentine flow field (PS-FF). The polarization curves, high-frequency resistance, low-frequency resistance, cathode pressure drop, and voltage stability of different flow fields were compared. Results show that, the maximum power density of the FCC-FF can be greatly increased compared to the MF-FF and the PS-FF. By introducing guide ribs into the corners of the flow field, the PEMFC performance could be further improved due to enhanced mass transfer, provided that the guide ribs do not bring in the negative effect of the under-rib weak flows. FCC-FF with an intermediate number of guide ribs, through balancing the in-plane and through-plane transport, show optimal performance of both power density and voltage stability at the expense of a moderately increased pressure drop, which is merited for practical applications. The present results are insightful for the development of the next-generation PEMFC towards higher power density.

Smooth control strategy for emergency switching of multi-port flexible interconnected distribution system modes

IntroductionWith the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system.MethodsThis paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage (Udc-Q) control port, a reselection method for the Udc-Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage.ResultsSimulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions.DiscussionThe control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.

Short-term voltage stability analysis in power systems: A voltage solvability indicator

This paper presents an analytical study for short-term voltage stability based on a novel indicator. First, it is shown that the reduced Jacobian matrix of network equations can be transformed to obtain an approximately symmetric matrix which is usually positive definite when the system under study is stable. The minimum eigenvalue of the matrix is suggested as a short-term Voltage Solvability Indicator (VSI). Then, properties of the reduced conductive matrix and susceptance matrix which have significant impacts on VSI are examined. The focus of the study is power systems with multiple constant power injections, while VSI can also be applied to analyze the impact of generic power system models. It is shown that as the penetration level of constant power injections increases, VSI decreases and the system becomes more vulnerable to voltage collapse. Finally, the relationship between the eigenvalues of the reduced Jacobian matrix and the structure-preserving Jacobian matrix is established. As a result, the suggested VSI can be efficiently computed by exploring the sparsity of structure-preserving Jacobian matrix of network equations. Case studies of several test systems including a simplified 575-machine 8117-bus East China power system are reported, demonstrating the effectiveness and practicability of the proposed method.

A Predictive Model Using Long Short-Time Memory (LSTM) Technique for Power System Voltage Stability

The stability of the operation of the power system is essential to ensure a continuous supply of electricity to meet the load of the system. In the operational process, voltage stability (VS) should be recognized and predicted as a basic requirement. In electrical systems, deep learning and machine learning algorithms have found widespread applications. These algorithms can learn from previous data to detect and predict future scenarios of potential instability. This study introduces long short-term memory (LSTM) technology to predict the stability of the nominal voltage of the power system. Based on the results, the recommended LSTM technology achieved the highest accuracy target of 99.5%. In addition, the LSTM model outperforms other machine learning (ML) and deep learning techniques, i.e., support vector machines (SVMs), Naive Bayes (NB), and convolutional neural networks (CNNs), when comparing the accuracy of the VS forecast. The results show that the LSTM method is useful to predict the voltage of an electrical system. The IEEE 33-bus system indicates that the recommended approach can rapidly and precisely verify the system stability category. Furthermore, the proposed method outperforms conventional assessment methods that rely on shallow learning.

Design, modeling, and validation of a 0.5 kWh flywheel energy storage system using magnetic levitation system

The flywheel energy storage system (FESS) has excellent power capacity and high conversion efficiency. It could be used as a mechanical battery in the uninterruptible power supply (UPS). The magnetic suspension technology is used in the FESS to reduce the standby loss and improve the power capacity. First, the whole system of the FESS with the magnetic levitation system is introduced, and the control diagrams of the charging/discharging processes are developed. Moreover, the force modeling of the magnetic levitation system, including the axial thrust-force permanent magnet bearing (PMB) and the active magnetic bearing (AMB), is conducted, and results indicate that the magnetic forces could stably levitate the flywheel (FW) rotor. The stator part and the FW rotor are analyzed using the FEM model, and the results could satisfy the requirements on stress and strength of the FESS at the rated speed. Then, the control models are established to accomplish the rapid charging/discharging functions of the FESS. Finally, experiments are performed to test the charging/discharging ability, and the results show that an excellent control current could enhance the charging/discharging efficiency so the stable DC link voltage could be outputted at the discharge process. Therefore, the test results prove that the designed FESS could be used as an auxiliary power supply for the UPS when the grid power fails.

Regulating the microstructure and hydrophilicity of cellulose nanofibers using taurine and the application in humidity sensor and actuator

Exploring the interrelationships between microstructure, hydrophilicity, humidity response, and material stability through molecular structure design and low-cost preparation, and manufacturing sensing and driving materials with a perfect balance between structures and properties is crucial for biomimetic robots and intelligent devices. Herein, inspired by the capillary phenomenon, the cellulose nanofiber (CNF) was grafted by using taurine to form the “micro cilia” microstructure on its surface (CNF-TAU). By controlling the cilia density and symmetry, the hydrophilicity of the materials was adjusted, and the water was allowed to quickly separate from the inside of the materials, which led to improving the humidity sensitivity and stability of materials. Concretely, compared with CNF, the puncture load, tearing strength, and elongation at break of the CNF-TAU-1.0 were improved by 11.95 times, 2.16 times, and 15.6 %, respectively, and excellent tensile strength was presented. Moreover, the hydrophilicity of CNF-TAU was first weakened and then enhanced with TAU content increasing. At last, the CNF-TAU-1.0 (WCA 55.43°) was selected and blended with single-layer graphene oxide (GO) to prepare CNF-TAU/GO composite films by vacuum filtration. The results indicated that the composite film had excellent mechanical (room and high humidity), humidity responsiveness, and humidity gradient-driven properties. In detail, the CNF-TAU/GO-10 % composite film had the generated stable induced voltage (68.4 mV), the fast response/recovery time (0.3 s/0.9 s), the large max. bending angle (178.4°), and the drive cycle stability (1000 cycles). Moreover, the composite film could be applied to produce biomimetic soft robots, such as human fingers and butterfly-shaped robots.

Advanced AI and renewable energy sources for unified rotor angle stability control

Maintaining a reliable electricity supply amidst the integration of diverse energy sources necessitates optimizing the stability of power systems. This paper introduces a groundbreaking method to enhance the efficiency and resilience of power grids. The increasing dependence on renewable energy sources poses significant challenges to traditional power networks, thereby demanding innovative solutions to uphold their stability and security. To address these challenges, we propose an architecture that seamlessly unifies dynamic, transient, and static rotor angle stability (RAS) controls into a single, streamlined system. Utilizing reinforcement learning and real-time decision-making, we present Lazy Deep Q Networks (LDQNs) as a novel approach to RAS control. LDQNs provide real-time rotor angle instructions to RAS devices, enabling precise and efficient stability management. The incorporation of mass-distributed energy storage further augments the system's responsiveness and flexibility, mitigating fluctuations and promoting overall stability. This study advances the application of AI methods to RAS control, building on prior research in frequency and voltage stability frameworks. The proposed system outperforms conventional RAS control methods by integrating LDQNs with mass-distributed energy storage, offering superior performance and adaptability. Case studies validate the effectiveness of the unified RAS framework, demonstrating its advantages over traditional approaches across various power system configurations.

Stability control and analysis of hydrogen production using a multi-terminal DC EV charging system with PV

In the shift towards sustainable energy, DC microgrids have emerged as efficient and reliable alternatives to traditional AC systems, particularly suited for low-voltage applications. The adoption of electric vehicles (EVs) in multi-terminal DC (MTDC) systems introduces challenges in maintaining voltage stability, particularly during master station failures. Our research introduces a decentralized control strategy with multi-mode switching to ensure stability under such conditions. We conduct a comprehensive stability analysis of MTDC systems heavily integrated with EVs, identifying the instability risks and proposing mitigation strategies. The effectiveness of our control approach is validated through simulation studies and a detailed state-space model. Additionally, we explore the integration of renewable energy sources and hydrogen production into MTDC systems for EV charging, which minimizes the need for a new power infrastructure and enhances renewable energy use. This integration optimally reduces renewable energy discard rates, thereby improving energy efficiency without compromising system performance. Our study provides practical insights and theoretical support for renewable energy-driven hydrogen production within DC microgrids. The proposed scheme's performance is validated through simulation studies using a state-space model.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

Highly Stable Perovskite Oxides for Electrocatalytic Acidic NOx− Reduction Streamlining Ammonia Synthesis from Air

AbstractElectrochemical nitrogen oxide ions reduction reaction (NOx−RR) shows great opportunity for ammonia production under ambient conditions. Yet, performing NOx−RR in strong acidic conditions remains challenging due to the corrosion effect on the catalyst and competing hydrogen evolution reactions. Here, we demonstrate a stable La1.5Sr0.5Ni0.5Fe0.5O4 perovskite oxide for the NOx−RR at pH 0, achieving a Faradaic efficiency for ammonia of approaching 100 % at a current density of 2 A cm−2 in a H‐type cell. At industrially relevant current density, the NOx−RR system shows stable cell voltage and Faradaic efficiency for >350 h in membrane electrode assembly (MEA) at pH 0. By integrating the catalyst in a stacked MEA with a series connection, we have successfully obtained a record‐breaking 2.578 g h−1 NH3 production rate at 20 A. This catalyst‘s unique acid‐operability streamlines downstream ammonia utilization for direct ammonium salt production and upstream integration with NOx sources. Techno‐economic and lifecycle assessments reveal substantial economic advantages for this ammonia production strategy, even when coupled with a plasma‐based NOx production system, presenting a sustainable complement to the conventional Haber–Bosch process.

Highly Stable Perovskite Oxides for Electrocatalytic Acidic NOx - Reduction Streamlining Ammonia Synthesis from Air.

Electrochemical nitrogen oxide ions reduction reaction (NOx -RR) shows great opportunity for ammonia production under ambient conditions. Yet, performing NOx -RR in strong acidic conditions remains challenging due to the corrosion effect on the catalyst and competing hydrogen evolution reactions. Here, we demonstrate a stable La1.5Sr0.5Ni0.5Fe0.5O4 perovskite oxide for the NOx -RR at pH 0, achieving a Faradaic efficiency for ammonia of approaching 100 % at a current density of 2 A cm-2 in a H-type cell. At industrially relevant current density, the NOx -RR system shows stable cell voltage and Faradaic efficiency for >350 h in membrane electrode assembly (MEA) at pH 0. By integrating the catalyst in a stacked MEA with a series connection, we have successfully obtained a record-breaking 2.578 g h-1 NH3 production rate at 20 A. This catalyst's unique acid-operability streamlines downstream ammonia utilization for direct ammonium salt production and upstream integration with NOx sources. Techno-economic and lifecycle assessments reveal substantial economic advantages for this ammonia production strategy, even when coupled with a plasma-based NOx production system, presenting a sustainable complement to the conventional Haber-Bosch process.

Nanocellulose and multi-walled carbon nanotubes reinforced polyacrylamide/sodium alginate conductive hydrogel as flexible sensor

Conductive hydrogels have been widely applied in human–computer interaction, tactile sensing, and sustainable green energy harvesting. Herein, a double cross-linked network composite hydrogel (MWCNTs/CNWs/PAM/SA) by constructing dual enhancers acting together with PAM/SA was constructed. By systematically optimizing the compositions, the hydrogel displayed features advantages of good mechanical adaptability, high conductivity sensitivity (GF = 5.65, 53 ms), low hysteresis (<11 %), and shape memory of water molecules and temperature. The nanocellulose crystals (CNWs) were bent and entangled with the backbone of the polyacrylamide/ sodium alginate (PAM/SA) hydrogel network, which effectively transferred the external mechanical forces to the entire physical and chemical cross-linking domains. Multi-walled carbon nanotubes (MWCNTs) were filled into the cross-linking network of the hydrogel to enhance the conductivity of the hydrogel effectively. Notably, hydrogels are designed as flexible tactile sensors that can accurately recognize and monitor electrical signals from different gesture movements and temperature changes. It was also assembled as a friction nanogenerator (TENG) that continuously generates a stable open circuit voltage (28 V) for self-powered small electronic devices. This research provides a new prospect for designing nanocellulose and MWCNTs reinforced conductive hydrogels via a facile method.

A dynamic reference voltage adjustment strategy for Open-UPQC to increase hosting capacity of electrical distribution networks

Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

A Redundant Secondary Control Scheme to Resist FDI Attacks in AC Microgrids

Hierarchical control of AC microgrids, despite providing stable voltage, frequency, and optimal power distribution, is susceptible to false data injection (FDI) attacks on its secondary control. Electric power systems in countries such as the United States and Venezuela have suffered cyberattacks. This paper proposes a redundant secondary control method to resist FDI attacks. This method uses distributed generators (DGs) online as redundant units on standby, generating dynamic corrections to the attacked DGs, thus eliminating the attack impact. It effectively addresses the numerical tampering and addition brought by FDI to the frequency and voltage setting values of the secondary control, ensuring the microgrid can still output stable frequency and voltage. The principle of this control method is direct and practical; it doesn't require complex controllers and can respond quickly to FDI attacks. Then, focusing on a microgrid system containing six DGs, the setting values of the microgrid system are tampered by multiple values in different time periods, the simulation results confirm that the proposed method can effectively resist FDI attacks under various scenarios. Finally, FPGA-in-the-loop experiments further verify the effectiveness of the proposed method.

Swarm‐intelligence‐based coordinated control of electric heatings for voltage stabilization with zero communication burden

AbstractThe movement towards electrification is entailing deep changes in power systems. On the demand side, the adoption of electric heating (EH) has grown rapidly in recent years. To eliminate the voltage fluctuations caused by the random switching‐on/‐off behaviors of EHs in some application scenes with special‐power‐line in low voltage distribution networks (LVDNs), a swarm‐intelligence‐based coordinated control approach of EHs for voltage stabilization with zero communication burden while guaranteeing the users’ thermal comforts is researched. A novel bird‐perch‐on‐branch (BPB) ‐based swarm intelligence theory is proposed, which reveals the mapping relation between local observations and global states. On this theoretical basis, a multi‐agent reinforcement learning (MARL) framework is developed for the coordinated dispatch of EHs, where the deep‐Q‐network (DQN) algorithm is adopted to learn and generate the optimal control policy for each EH. The implementation of the proposed MADQN‐BPB approach is described based on the principle of centralized‐training and decentralized‐execution. Comparative control performances of MADQN‐BPB with a commercially available approach, and the global optimal solution are evaluated. Simulation results verify the capability of MADQN‐BPB in voltage stabilization with zero communication burden. Its control performance is close to that of the global optimal solution, and is scalable to the variations of environment.

(Invited) Electrolysis for Sustainable Ethylene at Dioxycle

Industrial emitters are responsible for more than a third of global emissions. The central mission at Dioxycle is to divert industrial carbon emissions from entering the atmosphere and instead transform them into ethylene. Ethylene has a growing annual demand currently in excess of 150 Mton globally, corresponding to a $170 bn market, and is used in a wide range of consumer goods including fabrics, plastics, and construction materials. Electrified chemical manufacturing is a natural choice for generating sustainable products, but the biggest challenges to its widespread adoption include scalability of production and cost competitivity with fossil-based alternatives. A key difference between academia and industry is the critical focus on the factors that most directly influence the cost of the ultimate product. For electrified chemical manufacturing, this translates to voltage and long-term stability. For example, in a two-electron process such as water electrolysis, an overpotential of 1 V (total voltage 2.23 V) adds an additional 26.6 kWh or roughly $4 to the price of H2 per kg. For comparison, nonrenewable H2 produced from steam methane reforming can be sold for $1/kg H2. Additionally, electrochemical production must scale rapidly to meaningfully compete with their fossil-based alternatives. For a startup in particular, development and research targets must always have these considerations at the forefront and be able to adapt quickly to the latest results. While there are many challenges ahead, the growth of this field and the partnerships between academia and industry will be crucial to achieving our climate goals.

(Invited) Understanding the Effect of Cell Assembly and Operation on AEM Electrolyzer Performance and Durability

Water electrolysis to produce hydrogen is gaining significant attention due to a significant decrease in the cost of renewable energy sources such as solar, wind, and tidal etc. Among the existing water electrolysis technologies, anion exchange membrane water electrolysis has recently emerged due to its potential advantages over proton exchange membrane electrolysis and traditional alkaline electrolysis. Anion exchange membrane electrolyzers (AEMELs) can allow for the use of PGM-free electrocatalysts and low-cost component materials due to its less corrosive alkaline environment while also enabling electrochemical H2 compression.An overwhelming majority of the research that is going on related to AEMELs is the synthesis of advanced functional nanomaterials – e.g. new catalysts and membranes. Though these are critically important components of the cell, their function is supported by a multitude of other factors. For example, it is well-known that water and gas transport in the electrodes is highly dictated by the porous transport layers. The electrode fabrication technique controls the electrode morphology. The synthesis method for the catalyst determines its porosity and structure. The cell gasketing controls compression. The operating current density and temperature affect stress at the material and electrode level, and dictate other properties such as exchange current, ionic conductivity, water uptake, diffusivities, etc. Many of these properties are less reported and will be the subject of this presentation.This presentation will focus on understanding how various decisions that are made regarding electrode fabrication, cell assembly and cell operation affect the operating voltage and voltage stability of AEM electrolyzers. The goal of this talk is to combine electrochemical and physical characterization data to develop a series of guiding principles for AEMEL operation that can be used across polymer chemistries and catalyst selection.