Real-life Operating Conditions Research Articles

(Invited) Realizing the Lowest Bandgap and Exciton Binding Energy in a Two-Dimensional Lead Halide System

3-dimensional hybrid halide perovskites have been intensely researched because of their extraordinary optoelectronic properties with spectacular quantum efficiencies arising primarily from their suitable bandgaps to harvest the solar spectrum, low excitonic binding energies, and low-cost synthetic processes. However, stabilizing these materials under real-life operating conditions has proven to be a challenge, leading to extensive explorations of lower dimensional analogous hybrid halide materials that are often more stable, though generally having considerably higher bandgaps and excitonic binding energies. I shall present some of our investigations leading to the synthesis of a highly moisture-stable 2-dimensional hybrid lead halide material with the lowest bandgap and exciton binding energy within this class of compounds.1 I shall detail the origin of such a low bandgap and exciton binding energy, identifying the most critical control parameters to determine these properties. In addition, I shall also present preliminary results to suggest the beneficial impacts of incorporating the same organic ligand while making a solar photovoltaic device based on three-dimensional hybrid lead halide perovskites.Reference: Debasmita Pariari, Sakshi Mehta, Sayak Mandal, Arup Mahata, Titas Pramanik, Sujit Kamilya, Arya Vidhan, Tayur N. Guru Row, Pralay K. Santra, Shaibal K. Sarkar, Filippo De Angelis, Abhishake Mondal, and D. D. Sarma, Realizing the Lowest Bandgap and Exciton Binding Energy in a Two-Dimensional Lead Halide System, J. Am. Chem. Soc. 145, 15896 (2023).

Tribological behavior evaluation of poly-ether-ether-ketone (PEEK) in dry journal bearing on shaft wear test

This paper investigated the tribological behavior of pure PEEK playing as a dry journal bearing, sliding against stainless steel, under real-life operating conditions in a wear test lasting 2 h. Four conditions were carried out with different magnitudes of normal load and sliding speed, while keeping a constant PV parameter. Sample topography assessments were performed with scanning electron microscopy and white light interferometry techniques. In addition, PEEK samples were also evaluated by Fourier Transformed Infrared Spectroscopy and thermal analyses by Differential Scanning Calorimetry. The coefficient of friction (COF) changed throughout the wear test and exhibited a running-in period followed by a transition, with an increase of COF, and then a tendency to reach a steady-state. Moreover, the COF behavior obtained suggests that there was an influence of the wear test parameters, mainly right after the running-in period — and the COF averages for the steady-state period were statistically equal. Stainless steel samples showed mild wear, while PEEK samples had a drastic alteration of their topography imposed by the wear test — including an increase in its degree of crystallinity. Nonetheless, the PEEK journal bearing samples did not fail from wear and their mass losses were small. Polymeric wear particles were generated and were found weakly adhered to both the body and the counter body. PEEK exhibited a lumpy transfer process — nonetheless, SEM images evaluation was useful to verify that these particles were eventually merged during sliding, and kneaded in the convex-concave contact, which provided them a film-like appearance. Furthermore, adhesive and abrasive wear mechanisms were identified, which were shown to be related to the journal bearing on shaft wear test arrangement.

Microscopic railway capacity assessment of heterogeneous traffic under real-life operational conditions

A key strategy for increasing railway capacity is utilizing infrastructure more efficiently. While much research has been completed on methods for assessing railway capacity, very little has focused on the details of capacity utilisation, such as assessing the various ways trains use capacity, the impacts of specific blocking time components, and how train dynamics (accelerating, cruising, braking, and dwelling) affect capacity.This paper presents a methodology for comparing planned occupancy to actual occupancy under real operations and applies it in a case study. The methodology is based on identifying a critical path which represents an extension of bottleneck concept presented in UIC leaflet 406. The methodology was applied in a case study to determine the specific blocking times and train dynamics which cause a blocking time gap for a sequence of trains, both a-priori and a-posteriori, after considering the operational variations. The analysis of real operations with variations in train trajectories shows that capacity occupation is mostly influenced by train sequence heterogeneity in the original schedule. The varying effects of operations have a smaller but relevant impact. The methods developed in this paper can be used to help assess railway capacity under real operations.

Friction and Wear of the Piston Ring – Cylinder Liner System with Artificially Aged Ultra-low Viscosity Engine Oils

This study aims to investigate the performance of artificially aged prototype engine oils through friction and wear experiments. Experiments were performed on a piston ring – cylinder liner model system with boundary conditions derived from real-life operating conditions. The experimental design implemented two prototype oils (SAE 0W-12 and 0W-16) in unaltered and artificially aged form. An additional fully formulated off-the-shelf engine oil (SAE 0W-20) was also aged and analyzed as reference. Oil samples were artificially aged in a custom rig, to simulate long-term in-engine use through thermal cycling at 180 °C. Fourier Transformed Infrared Spectroscopy of the lubricant samples highlighted a depletion of zinc dialkyl-ditiophosphate antiwear additives in all cases, which is comparable to a selected in-service oil. Oxidation was also measurable, albeit lower compared to the in-service sample. Averaged friction coefficients showed a ranking of aged 0W-12 < aged 0W-16 < unaltered 0W-12 < unaltered 0W-16. A decrease in surface roughness was experienced with aged oil samples, whereas unaltered 0W-16 oil produced an unexpected transition in the wear phenomenon and resulted in severe wear.

High-throughput parallel testing of ten photoelectrochemical cells for water splitting: case study on the effects of temperature in hematite photoanodes.

High-throughput testing of photoelectrochemical cells and materials under well-defined operating conditions can accelerate the discovery of new semiconducting materials, the characterization of the phenomena occurring at the semiconductor-electrolyte interface, or the understanding of the coupled multi-physics transport phenomena of a complete working cell. However, there have been few high-throughput systems capable of dealing with complete cells and applying variations in real-life operating conditions, like temperature or irradiance. Understanding the effects of the variations of these real-life operating conditions on the performance of photoelectrode materials requires reliable and reproducible measurements. In this work, we report on a setup that simultaneously tests ten individual, identical photoelectrochemical cells whilst controlling temperature. The effects of temperature from 26 to 65 °C were studied in tin-doped hematite photoanodes for water splitting - as a reference case - through cyclic voltammetry and electrochemical impedance spectroscopy. The increase of surface-state-mediated charge recombination with temperature mainly penalized the energy conversion efficiency due to the reduction of the photovoltage produced. For parallel measurements in the ten individual cells, standard deviations from 20 to 60 mV for the onset potentials and less than 0.2 mA cm-2 for saturation current densities quantified the reproducibility of the results.

The Application of an Artificial Neural Network as a Baseline Model for Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

Abstract Due to high penetration of renewables, the EU energy system is undergoing a transition from large-scale centralized generation toward small-scale distributed generation. The increasing share of intermittent renewables such as solar and wind has become the main driver for dispatchable distributed energy generation technologies to maintain the grid flexibility and stability. In this context, micro gas turbines (MGTs) with high fuel and operation flexibility could play a crucial role to guarantee the grid stability, enabling deeper penetration of the intermittent renewable energy sources. Despite this, the MGT market is still considered to be niche, and there are R&D&I challenges that need to be addressed to further promote this technology in distributed generation applications. Innovative MGT cycles based on a cycle humidification concept can be considered to obtain higher system performance. However, given the fact that MGTs are installed close to the consumption points, where they are operated by nontechnical prosumers with very limited access to maintenance services, they should also offer high availability and reliability to avoid unexpected outages and secure the supply. Therefore, intelligent monitoring systems are needed that can support nonexpert end-users to detect degradation and plan maintenance before a breakdown occurs. In this study, we investigated and developed advanced methods based on artificial neural networks (ANNs) for condition monitoring of a humidified MGT cycle under real-life operational conditions. To create a high-performing model, extensive data preprocessing has been conducted to remove data outliers and select optimum model features, which provide best results. Additionally, the model hyperparameters such as learning rate, momentum, and number of hidden nodes have been altered to achieve the most accurate predictions. The results of this study have provided a baseline ANN model capable of conducting condition monitoring of a micro humid air turbine (mHAT) system, which will be applied to additional studies in the future.

Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations

A prescriptive simulation model of a process operator’s decision making assisted with an artificial intelligence (AI) algorithm in a technical system control loop is proposed. Situations fraught with a catastrophic threat that may cause unacceptable damage were analyzed. The operators’ decision making was interpreted in terms of a subjectively admissible probability of disaster and subjectively necessary reliability of its assessment, which reflect the individual psychological aspect of operator’s trust in AI. Four extreme decision-making strategies corresponding to different ratios between the above variables were distinguished. An experiment simulating a process facility, an AI algorithm and operator’s decision making strategy was held. It showed that depending on the properties of a controlled process (its dynamics and the hazard onset’s speed) and the AI algorithm characteristics (Type I and II error rate), each of such strategies or some intermediate strategy may prove to be more beneficial than others. The same approach is applicable to the identification and analysis of sustainability of strategies applied in real-life operating conditions, as well as to the development of a computer simulator to train operators to control hazardous technological processes using AI-generated advice.

Does the circularity end justify the means? A life cycle assessment of preparing waste electrical and electronic equipment for reuse

Waste Electrical and Electronic Equipment (WEEE) pose a significant threat to the environment and public health due to the hazardous substances and the valuable and often scarce resources they contain. Policies for WEEE recycling have been adopted in most countries, while in the European Union the WEEE Directive renders preparing for reuse as a priority, although specific quantitative targets have not been imposed so far. Preparation for reuse to be effective requires improved WEEE collection networks and additional infrastructure, such as sorting and repair centres, all of which consume energy and materials, which also have environmental impacts. Moreover, refurbished appliances may be less energy efficient and there is a need to assess whether this benefit justifies the preparation for reuse burdens. The aim of this study was to investigate, under real-life operating conditions, whether the life cycle environmental impacts resulting from the preparation for reuse of WEEE are offset by the benefits of avoiding a new product purchase, which may have a lower energy consumption during its use lifespan. Data for desktops, laptops, monitors, printers, smartphones, and vacuum cleaners were collected from the two full-scale sorting centres in Greece, and life cycle assessments were performed. Results indicate that repair and reuse of smartphones leads to a reduction of their environmental impacts up to 25 %. To a lesser degree, laptops, desktops, and monitors, lead to reduce environmental impacts from −1.4 % to 19.2 %. For printers, the reductions are almost negligible, from 0.3 % to 3 %. On the other side, the repair and reuse of vacuum cleaners resulted in an increase of almost all their environmental impacts instead, even more than 70 % for marine and freshwater eutrophication. These findings can be used to the policy discourse for specific preparing for reuse targets and to inform decision-making. This could lead to prioritising specific categories for repair such as smartphones while avoiding specific WEEE categories from preparing for reuse such as vacuum cleaners and instead promote their substitution.

Predicting drivers’ route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network

In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and other characteristics of the respective service areas. Hence, the actual stop sequences chosen by an experienced human driver may be potentially preferable to the theoretical shortest-distance routing under real-life operational conditions. Thus, being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery. This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers’ historical delivery trajectory data. In addition to the commonly used encoder–decoder architecture for sequence-to-sequence prediction, we propose a new attention mechanism based on an alternative specific neural network to capture the local pair-wise information for each pair of stops. To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost. Results from an extensive case study on real operational data from Amazon’s last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder–decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. Compared to benchmark models, the proposed model can increase the average prediction accuracy of the first four stops from around 0.229 to 0.312, and reduce the disparity between the predicted route and the actual route by around 15%.

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types

Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of 3.34 × 10 − 8 and high training and testing regression of 100% and 99.98%, respectively.

Design, commissioning and operation of a mini hybrid parabolic trough solar thermal power plant for direct steam generation

Parabolic Trough Collectors (PTCs) are the most mature, reliable and widespread among Concentrated Solar Power (CSP) technologies for electricity generation. The conventional power plants relying on PTCs use synthetic oil as Heat Transfer Fluid (HTF) which has limited life span, has limited operation temperature, is expensive and harmful to the environment. In the present research work, a new perspective has been developed to directly generate steam in the collectors, eliminating the need for HTF in the solar field and avoiding auxiliary heat exchangers. A mini hybrid Direct Steam Generation PTC power plant having a boiler as a backup that can be operated with biogas or natural gas is installed at Ecole Nationale d'Ingénieurs de Tunis (ENIT) within the framework of REELCOOP project (Renewable ELectricity COOPeration). The description, commissioning and operation of the solar plant are presented in this paper. The plant is designed and tested under real-life operating conditions in the south Mediterranean region. The feasibility of the DSG process in horizontal parabolic trough collectors has been confirmed and an important know how has been acquired regarding the operation of DSG solar plant. The main instructions for the operation of the solar plant have been presented including start-up and shutdown procedures of the plant, as well as the safety measures to be considered.The development of this plant is aimed to address the challenge of coupling intermittent solar radiation and the presence of a two-phase fluid in the absorber tubes. An important know how has been acquired regarding the operation of DSG solar plants in solar only mode and in hybrid mode which allowed to balance strengths and weaknesses of solar and biogas/natural gas technologies. By combining both systems, we have tackled the problem of the controllability of the energy supply while exploiting as much as possible renewable sources.

Constant mixing temperature test of a fin-and-tube latent heat thermal energy storage

Thermal energy accumulation is one of the ways how to optimize heat production processes and how to balance the supply and demand of heat in distribution systems. This article presents a design of a fin-and-tube latent heat thermal energy storage (LHTES), which combines high thermal energy storage density and scalability. A computational model that used lumped heat capacities was tuned using the experimental data. The numerical model proved to be simple yet precise. A new constant mixing temperature test was designed and performed with the LHTES. Unlike standard constant flow rate charge/discharge test, this test provided valuable information about what to expect in the real-life operation conditions. From the tests and data from simulations, it was concluded that the LHTES would perform better in terms of its capacity utilization if it operated at lower output power than in the laboratory circuit. This indicates that a smaller, and thus more cost-effective, LHTES could be employed in the laboratory circuit with virtually the same utility to the system if its heat transfer characteristics were improved.

Durability study of direct methanol fuel cell under accelerated stress test

Durability of direct methanol fuel cell (DMFC) is one of the key issues for its commercial applications. This work proposes accelerated stress test (AST) protocols including high operation temperature, high current density, dry-wet cycle to resemble real-life operation conditions and shorten evaluation time. The cell with Nafion membrane is operated under a relatively high temperature of 88 °C with ambient pressure air, and an unprecedented constant current density of 400 mA cm−2. After 850 h of operation, the cell still survives with a permanent degradation rate of about 48 μV hr−1. From the in-situ electrochemical diagnostics, no pronounced ohmic loss occurs, indicating stable membrane/electrode interface. However, both anode and cathode performance degradation are found. The anode exhibits high mass transfer resistance at high current density due to degradation of gas diffusion layer (GDL). The GDL compensated anode potential loss, induced by the degradation of PtRu/C catalyst, is comparable to that of cathode at whole current density region. The degradation of cathode is primarily attributed to the loss of electrochemical surface area (ECSA) that is associated with the degradation of Pt/C and PtRu/C catalyst.

Insights into Degradation of the Membrane-Electrode Assembly Performance in Low-Temperature PEMFC: the Catalyst, the Ionomer, or the Interface?

Here, we report a study on the structural characteristics of membrane electrode assembly (MEA) samples obtained from a low-temperature (LT) polymer electrolyte membrane (PEM) fuel cell (FC) stack subjected to long-term durability testing for ∼18,500 h of nominal operation along with ∼900 on/off cycles accumulated over the operation time, with the total power production being 3.39 kW h/cm2 of MEA and the overall degradation being 87% based on performance loss. The chemical and physical states of the degraded MEAs were investigated through structural characterizations aiming to probe their different components, namely the cathode and anode electrocatalysts, the Nafion ionomer in the catalyst layers (CLs), the gas diffusion layers (GDLs), and the PEM. Surprisingly, X-ray diffraction and electron microscopy studies suggested no significant degradation of the electrocatalysts. Similarly, the cathode and anode GDLs exhibited no significant change in porosity and structure as indicated by BET analysis and helium ion microscopy. Nevertheless, X-ray fluorescence spectroscopy, elemental analysis through a CHNS analyzer, and comprehensive investigations by X-ray photoelectron spectroscopy suggested significant degradation of the Nafion, especially in terms of sulfur content, that is, the abundance of the -SO3- groups responsible for H+ conduction. Hence, the degradation of the Nafion, in both of the CLs and in the PEM, was found to be the principal mechanism for performance degradation, while the Pt/C catalyst degradation in terms of particle size enlargement or mass loss was minimal. The study suggests that under real-life operating conditions, ionomer degradation plays a more significant role than electrocatalyst degradation in LT-PEMFCs, in contrast to many scientific studies under artificial stress conditions. Mitigation of the ionomer degradation must be emphasized as a strategy to improve the PEMFC's durability.

Wind-assisted, electric, and pure wind propulsion – the path towards zero-emission RoRo ships

ABSTRACT Electrical and wind propulsion, together with energy stored in batteries and renewable energies harnessed onboard, can lead the way towards zero-emission ships. This study compares wind propulsion solutions and battery storage possibilities for a RoRo ship operating in the Baltic Sea. The ship energy systems simulation model ShipCLEAN is used to predict the performance of the zero-emission ship in real-life operating conditions. The study showcases how ships can be transferred from a conventional, diesel-powered to a zero-emission ship. For the zero-emission ship, all energy needed for auxiliaries and propulsion is taken from renewable sources onboard or from batteries. Challenges and opportunities, as well as necessary adaptions of the route and logistics, are discussed. Results of the study present which wind propulsion technology is the most suitable for the example RoRo ship, and how the installation of suitably sized battery packs for zero-emission operation affects the cargo capacity of the ship.

The Bacteria Addition Study to Batik Wastewater Industries In pH Performance, and Removal of Ammonia and COD

The production of batik generates a large volume of wastewater with complex nature and characteristics such as high temperature, pH, BOD, COD, and TSS. In Jababeka Industrial Estate, Wastewater from batik industries is treated by going through a facultative equalization tank before going to the next treatment units. Hypothetically, by optimizing the facultative equalization tank, it will improve the effluent quality and increase COD and Ammonia’s removal efficiency. This research used 4 microbial products that are commercially available and can be applied easily in real-life operating conditions. The study aimed to determine the effect of bacteria addition and its removal efficiency for batik wastewater effluent from batik industries in Jababeka Industrial Estate. the parameters measured were pH, concentration of COD and ammonia. The addition of bacteria reduced COD and Ammonia concentration in the wastewater and changed the wastewater’s pH and color. The result indicates that for high COD and ammonia loads, the M-Aerobic bacteria is the best type of bacteria with a removal efficiency of 63.39% and 99%, respectively. However, ANOVA analysis indicates that there is no statistically significant difference between the treatment groups. It is recommended for this experiment to be repeated in a continuous reactor.

Modeling photovoltaic-electrochemical water splitting devices for the production of hydrogen under real working conditions

Photoelectrochemical splitting of water is potentially a sustainable and affordable solution to produce hydrogen from sun light. Given the infancy stage of technology development, it is important to compare the different experimental concepts and identify the most promising routes. The performance of photoelectrochemical devices is typically measured and reported under ideal irradiation conditions, i.e. 1 sun. However, real-life operating conditions are very different, and are varying in time according to daily and seasonal cycles. In this work, we present an equivalent circuit model for computing the steady state performance of photoelectrochemical cells. The model allows for a computationally efficient, yet precise prediction of the system performance and a comparison of different devices working in real operating conditions. To this end, five different photoelectrochemical devices are modeled using experimental results from literature. The calculated performance shows good agreement with experimental data of the different devices. Furthermore, the model is extended to include the effect of illumination and tilt angle on the hydrogen production efficiency. The resulting model is used to compare the devices for different locations with high and low average illumination and different tilt angles. The results show that including real illumination data has a considerable impact on the efficiency of the PV-EC device. The yearly average solar-to-hydrogen efficiency is significantly lower than the ideal one. Moreover, it is dependent on the tilt angle, whose optimal value for European-like latitude is around 40°. Notably, we also show that the most performing device through the whole year might not necessarily be the one with highest sun-to-hydrogen efficiency for one-sun illumination.

Low-cost poly(vinyl formal) for heavy metal removal from water of a polluted river

Abstract Heavy metal pollution in drinking water is a serious health risk, particularly in developing countries. Membranes of poly(vinyl formal) (PFA) were synthesized to explore their use for heavy metal removal from water samples from the polluted Mexican river ‘Atoyac.’ A single incubation step with PFA membranes for 24 h was sufficient to reduce lead, iron, manganese, arsenic, and thallium concentrations to acceptable levels for drinking water, according to the Mexican standards. PFA showed high adsorption capacities (Qe), even in the presence of multiple metals and low cation concentration. Mass transport phenomena were characterized as a function of the crosslinking degree and pH. To simulate real-life operation conditions, water fluxes across PFA membranes were characterized as a function of their crosslinking degrees and controlled external pressures. PFA membranes allowed fluxes at pressures of up to 2 kg/cm2 with no deformation or breaking. Tensile strength, strain stress, elastic deformation, and Young's modulus were assessed. In contrast with previous reports, no further PFA modifications were required to obtain an excellent adsorption performance, which results in lower production costs. The ease of PFA synthesis and handling, and low production cost make it an ideal material to be applied for remediating the polluted urban water.

Static and free vibration analyses and dynamic control of smart variable stiffness laminated composite plate with delamination

The present work is mainly focused on the static and dynamic analysis of curvilinear fiber laminated composite plate integrated with piezoelectric layers acting as actuators and sensors. Delamination is an inevitable phenomenon in composite structures encountered often in real life operating conditions. The presence of delamination within the structure may significantly affect the dynamic response due to the reduction of stiffness of the structure. An eight-noded isoparametric element using first-order shear deformation theory based finite element formulation is adopted for discretizing the domain. The accuracy and adaptability of the present FE model are established through various validation studies. Further, various parametric studies are conducted to investigate the effect of different delamination sizes and positions on the static and dynamic behavior of smart composite plates with curved as well as straight fiber laminates for various boundary conditions. It is observed that the curved fiber laminates provide the flexibility to adjust the fiber orientation within the lamina in such a fashion that frequency reduction due to delamination can be reduced. An attempt is made to effectively damp out the vibration of a smart delaminated plate with the use of active feedback control.

Batteries & Supercaps: In Situ and Operando Methods for Energy Storage and Conversion

Take a close look: This Special Collection is devoted to in situ and (in) operando methods developed and used for studying materials applied to electrochemical storage devices. Environmental concerns have pushed energy storage technologies toward higher sustainability and efficiency, and functional materials must evolve to meet these demands. This Special Collection concerns the recent evolution of methods and techniques used to understand the function of such materials, a necessary step towards the rational design of new energy storage technology with improved performance characteristics. Such characterisation methods would ideally give information about energy storage materials and components that are performing as they would in the operating device, as close to real-world applied conditions and with as much detail as possible. Even though it is often necessary to adapt the system being studied to the specific constraints of a characterisation tool to obtain detailed or high quality information, recent advances in instrumentation and methods are bringing us closer to being able to examine materials under representative real-life operating conditions. Such analytical approaches, which are also known as “in situ” and “(in) operando” methods, can provide unique insights on materials function under energy storage and delivery cycling conditions and are becoming increasingly popular in energy materials research. Both the number of available techniques applied to understand function of materials within working devices as well as the energy and spatial resolution gained by these techniques are growing. This Special Collection is devoted to these methods and features works highlighting such approaches as applied to electrochemical storage devices. While some works in this Special Collection highlight recent developments in the in situ and (in) operando characterisation techniques that have been successfully applied for more than a decade, such as X-ray and neutron diffraction and X-ray spectroscopy (Liang et al., Wu et al., Gustafsson et al., Chien et al., Lebens-Higgins), some new rising methods such as acoustic emission methods (Schweidler et al.), online gas analysis (Sångeland et al., Ab-El-Latif et al.), dilatometry (Michael et al.), and terahertz spectroscopy (Krotkov et al.), provide complementary information of the evolution of materials within systems, particularly regarding important insights into materials degradation mechanisms. Because instrumentation and techniques have advanced faster than terms used to describe many of these methods, we do not yet have an agreement within the community on language used to discuss these characterization conditions. The International Union of Pure and Applied Chemistry Committee “Recommendations for terms relating to materials characterization: Latin and other introduced terms” aims to clarify and resolve discrepancies for terms describing materials characterization, particularly for non-ambient conditions, within systems, and during change, as relevant for energy storage materials. The project committee invites you to contribute to this project and seeks input in particular into reviewing the list of terms used to describe sample characterisation conditions. To get involved, please see details at: https://iupac.org/projects/project-details/?project nr=2021-009-2-500. Vanessa Peterson is a Senior Principal Research and Neutron Instrument Scientist for powder diffraction at the Australian Centre for Neutron Scattering and an Honorary Professorial Fellow at the University of Wollongong. She obtained her Ph.D. in 2004 in conjunction with the neutron scattering group at the Australian Nuclear Science and Technology Organisation. She previously worked at the NIST Center for Neutron Research in the USA, and at the University of Sydney. She leads the “Energy Materials” research project from within the ACNS and specializes in characterization methods, particularly using real-time neutron and X-ray scattering to understand materials function. Lorenzo Stievano is Professor at University of Montpellier since September 2009. At ICGM, he is an expert in the spectroscopic characterization of functional inorganic materials and in the development of specific in situ and operando tools for the analysis of their working mechanisms. His research activities are currently focused on the fields of advanced Li-ion and post-lithium battery systems.