Ultrahigh-efficiency desalination via a thermally-localized multistage solar still
Passive vapor generation systems combining interfacial solar heating and vaporization enthalpy recycling enable high-efficient low-cost desalination.
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Passive vapor generation systems combining interfacial solar heating and vaporization enthalpy recycling enable high-efficient low-cost desalination.
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Concentrating solar power (CSP) plants convert energy from sunlight into electricity. Generally, a heliostat field is used to reflect sunlight and concentrate it onto a receiver, which heats the receiver and/or the media flowing through it. A flow loop transports the media through the system to a heat exchanger before returning the media back to the receiver to be reheated. The heat extracted by the heat exchanger is then converted into electricity using a conventional heat engine.
Discussed in order after the introduction are solar components and systems (collectors, storage, service hot water systems, space heating with liquid and air systems, space cooling, heat pumps and controls); computer programs for system optimization; local solar and weather data; a description of buildings and plants in Southern California applying solar technology; current Federal and California solar legislation; standards, codes and performance testing information; a listing of manufacturers, distributors, and professional services available in Southern California region; and information access. Finally, solar design check lists for those engineers who wish to design their own systems. The program for the Solar Workshop for the Plant Engineer, March 30, 1978, Los Angeles, California is included.
A solar absorption cooling system consisting of a flat plate collector, thermal energy storage tank, and absorption chiller is analyzed in this work. A dimensionless model is developed from the energy balance on each component and the chiller's characteristic performance curves. The model is used to determine the interaction and influence of different parameters such as tank size, solar collector area, chiller size, cooling load, cooling temperature, heat loss, and mass flow rates on the performance. From the analysis, smaller solar collector areas are required for lower cooling loads and smaller tank volumes. A specific cooling load of 1 x 10^-5 will require a specific solar collector area between two and six times larger, depending on the initial tank temperature, than the area required for a baseline system that considers typical commercial design and operation parameters. A similar behavior was observed for the specific tank volume. For the baseline system, the minimum specific area of the collector of 9.57 is achieved for an initial tank temperature of 1.19. For a cooling load of 1 x 10^-5, the optimum initial tank temperature will be 1.11 that results in a minimum specific solar collector area of 25.26. A specific tank volume of 4 x 10^-4 will also have an optimum initial tank temperature of 1.11 that minimizes the specific solar collector area to a value of 28.18. The approach and analysis in this work can be used to determine design parameters for solar absorption cooling systems based on a proper relation among system's dimensions to achieve optimum operation.
This paper presents an account of Trevi’s delivery of a 500 m3/day solar powered forward osmosis (FO) seawater desalination plant at the Ocean Science and Technology Park of the Natural Energy Laboratory of Hawaii Authority (NELHA). The project aimed to demonstrate the viability of solar thermal-powered desalination for agricultural applications through the integration of a 2MW micro-dish solar thermal array with a state-of-the-art FO system. Highlighted in the paper are the three distinct project phases; Planning and Design, System Construction, Installation & Testing followed finally by System Operation and Optimization. Results and decisions which led to the final plant design will be shared, highlighting how Trevi Systems succeeded in producing a zero-carbon FO seawater desalination plant with a projected Levelized Cost of Water (LCOW) estimate competitive with existing carbon-intensive RO technologies (based on some assumptions and the cost of heat which is required for FO.
Long-term thermal stability is one limiting factor that impedes the commercialization of perovskite solar cell. Inspired by our prior results from machine learning, we discover that coating a thin layer of 4,4’-dibromotriphenylamine (DBTPA) on top of a CH 3 NH 3 PbI 3 (MAPbI 3 ) thin film can improve the stability of the resultant perovskite solar cells. The passivated perovskite solar cells retained more than 96% of their initial power conversion efficiency over 1000 h at 85 °C in N2 atmosphere without encapsulation. Near-ambient pressure x-ray photoelectron spectroscopy (NAP-XPS) was employed to investigate the evolution of composition and evaluate thermal and moisture stability by in-situ studies. A comparison between pristine MAPbI 3 films and DBTPA-treated films shows the DBTPA treatment suppresses the escape of iodide and methylamine up to 150 °C under 5 mbar humidity. Furthermore, we have used ATR-FTIR and XPS to probe the interactions between DBTPA and MAPbI3 surface. The results prove that DBTPA coordinate with perovskite by Lewis acid-base and cation-π interaction. Compared with the 19.9% efficiency of pristine sample, the champion efficiency of passivated sample reaches 20.6%. Finally, our results reveal DBTPA as a new post-treating molecule that not only leads to the improvement of photovoltaic efficiency but also thermal and moisture stability.
Thermal stability of organic–inorganic hybrid perovskites (OIHPs) remains as one of the critical challenges against the stable operation of perovskite solar cells (PSCs) in direct sunlight with elevated temperatures. Here, we show that the addition of a polystyrene-co-polyacrylonitrile (SAN) copolymer can significantly enhance thermal stability of OIHPs and improve the stability of the corresponding PSCs by suppressing the migration of organic cations in OHIP. The methylammonium lead iodide (MAPI) with SAN incorporated within the perovskite layer featured a superior thermal stability compared to pure MAPI without SAN, only displaying an average of 5–15% decrease in PCE even after continuous thermal aging for 24 h at 100 °C. The secondary ion mass spectrometry revealed that the thermal degradation of the pure MAPI was largely associated with MA+ out-migration. Conducting atomic force microscopy analysis further indicated that the incorporated SAN led to a suppression of ionic currents present at the grain boundaries of the perovskite film, which was understood by high immiscibility between SAN and MA+ components as confirmed by the experimentally estimated Flory–Huggins parameter between them. This study newly identifies a potential of using polymer grain encapsulation for enhancing thermal stability of OIHPs and their solar cell performance by suppressing the out-diffusion of cationic organic components.
Adaptability, lightweight, and low cost of materials make flexible perovskite solar cells essential for the perovskite technology to breach commercial photovoltaic market. Roll-to-roll manufacturing has great potential for high-throughput perovskite manufacturing; however, conventional annealing methods are not capable of producing flexible perovskite solar cells at scale. Rapid thermal annealing (RTA), a radiative method, is utilized on roll-to-roll lines within many different industries and has shown promising potential for perovskite annealing. Here, we introduce a blade-coated perovskite thin film on flexible ITO-coated PET annealed via RTA that reduces post-deposition processing time from ~150 seconds to ~14 seconds. Performance was not sacrificed for scalability as a perovskite solar cell was produced with a power conversion efficiency (PCE) of 14.58%.
This project achieved its overarching goal of accelerating the discovery and validation of solar thermochemical hydrogen (STCH) materials through a tightly integrated approach that combined high-throughput computational screening, advanced machine learning (ML), and experimental testing. Guided by the objectives outlined in the Statement of Project Objectives (SOPO), our work fulfilled all major milestones across four technical tasks and delivered scientific breakthroughs and practical tools that significantly exceeded the original scope of the project. We began by addressing the challenge of predicting material phase stability through machine learning. A novel Python module was developed to generate thousands of meaningful features from composition, structure, and electronic properties, enabling rapid and reproducible ML model development. Using these tools, we trained a model to predict temperature-dependent Gibbs energies (G(T)) for inorganic crystalline materials with near-chemical accuracy—roughly 40 meV/atom—marking the first such descriptor of its kind. We also introduced a new machine-learned tolerance factor, τ, that accurately predicted perovskite formability with over 90% success, outperforming traditional heuristic models, such as the Goldschmidt tolerance factor. These capabilities allowed for rapid and accurate predictions of phase stability across a vast oxide composition space, setting the stage for high-throughput thermodynamic screening. Building on this foundation, we conducted an extensive computational screening of candidate STCH oxide materials. Over 1.1 million perovskite compositions were evaluated using the τ descriptor, leading to the identification of more than 27,000 predicted stable structures. Using density functional theory (DFT), we refined over 68,000 multinary perovskite structures and computed oxygen vacancy formation energies for over 1,300 ternary and double perovskites. These calculations enabled us to isolate compounds with redox behavior consistent with STCH requirements and resulted in a public dataset now hosted on the Materials Project. Recognizing that thermodynamic screening alone is insufficient, we addressed kinetic limitations by developing a suite of tools to estimate transition state (TS) energies for key redox reactions. We implemented a novel bounding approach that provides lower and upper estimates of TS energies with dramatically reduced computational cost, requiring less than 10% of the CPU time of a full nudged elastic band (NEB) calculation while maintaining high accuracy. This enabled rapid evaluation of over 200 reaction pathways across 90 materials. To further accelerate screening, we developed a SISSO-based ML model to predict diffusion barriers with a 96.7% success rate in classifying fast vs. slow materials, supporting a robust, data-driven framework for assessing redox kinetics. Experimental validation was critical to confirming the predictive power of our models. We synthesized and tested a wide array of candidate materials, including Mn-doped hercynite and several Gd- and La-based perovskites. Notably, Sr 0.4 Gd 0.6 Mn 0.6 Al 0.4 O 3 (SGMA) and Gd 0.5 La 0.5 Co 0.5 Fe 0.5 O 3 (GLCF) emerged as leading STCH materials, exhibiting robust redox cycling and high hydrogen yields exceeding 150 µmol H 2 /g per cycle. These materials also retained over 50% of their hydrogen productivity under high-conversion conditions (H 2 O:H 2 = 1333:1), demonstrating strong thermodynamic favorability and promising performance under industrially relevant scenarios. Additional candidates, such as La 2 MnNiO 6 (L2MN), were found to produce even higher yields than ceria under standard STCH conditions. Our collaborators at Sandia National Laboratories confirmed these findings using high-temperature X-ray diffraction and thermogravimetric analysis, observing stable phase evolution and reversible redox activity. In several respects, the project went beyond the goals initially outlined in the SOPO. We published 17 peer-reviewed articles, including a large dataset of over 66,000 theoretical perovskites and a new structure prediction method (SPuDS-DFT) that accurately identifies ground-state structures at a fraction of the cost of traditional DFT. We demonstrated that our machine-learned G(T) model offers accuracy rivaling quasiharmonic calculations while being orders of magnitude faster. In partnership with the Materials Project, we made our datasets openly available, providing a powerful new resource for the broader materials science community. The combined computational and experimental advances of this project represent a significant advance in STCH materials discovery. By creating a robust, generalizable, and open workflow for thermodynamic and kinetic screening, and validating key findings through synthesis and reactor testing, we have provided a practical and scalable pathway for the rapid identification of new redox-active materials. The tools, data, and materials developed under this project are already supporting ongoing research and have laid the groundwork for the next generation of solar fuel technologies.
Pumped Thermal Energy Storage (PTES) is an electricity storage system that converts electricity into thermal energy which is stored and later transformed back into electricity. Previous work has illustrated that particles have low capital costs and can be operated over a wide range of temperatures. PTES with particle storage achieves higher round-trip efficiency and specific power output than when molten salt thermal energy storage is used. This article explores hybrid systems that combine PTES with Concentrating Solar Power (CSP). Hybrid systems share the majority of components thereby reducing costs compared to two stand-alone devices. In addition, hybrid systems can provide multiple services (such as renewable power generation and electricity storage services). Using particle thermal storage in these hybrid concepts provides freedom in choosing the design conditions, since a wide range of operating temperatures is allowable, therefore making it possible to identify hybrid system designs that have good performance. In this article, two concepts for hybrid PTES-CSP are introduced. Thermodynamic models are developed and these are used to evaluate the performance of two hybrid systems. These models account for turbomachinery efficiency, and approach temperature and pressure loss in heat exchangers, as well as other sources of inefficiency, such as motor-generator losses, and air fan power. The “Solar Top-Up” Concept uses CSP to increase the temperature delivered by the charging heat pump. The discharging system uses a topping gas cycle and a bottoming steam cycle to fully exploit the available energy. Using solar heat to increase the maximum temperature from 750 K to 1100 K increases the round-trip efficiency from 41 % to 62 % and the specific work output from 88 kJ/kg to 301 kJ/kg. The second concept is a “Dual-Mode” device which provides both electricity storage and solar electricity generation with the same set of components. The PTES-mode and CSP-mode performance are optimized at different design values but careful parameter selection leads to good performance of both modes: one design produces PTES round-trip efficiency > 60 %, CSP heat engine efficiency > 40 %, and specific work outputs > 150 kJ/kg (for both cycles), when the particle receiver temperature is > 1200 K and maximum heat pump temperature is 1100 K.
Adaptability, lightweight, and low cost of materials make flexible perovskite solar cells essential for the perovskite technology to breach commercial photovoltaic markets. Roll-to-roll manufacturing has great potential for high-throughput perovskite manufacturing; however, conventional annealing methods are not capable of producing flexible perovskite solar cells at scale. Conventional methods are time consuming and may compromise functionality of flexible substrates under tension. Here, we introduce a bladecoated perovskite thin film on flexible ITO-coated PET annealed via rapid thermal annealing (RTA) that reduces post-deposition processing time from ~150 s to ~14 s. As a result, performance was not sacrificed for scalability as a perovskite solar cell was produced with a power conversion efficiency (PCE) of 14.58%.
There is an increasing need to reduce fossil fuel consumption used for industrial process heat to slow the effects of climate change. Using solar thermal heat is a viable way to replace fossil fuel use, but solar industrial process heat plants have limited implementation due to large upfront costs and inefficiencies from the inherent variability from solar energy. Having more flexible and optimized control over these plants can enable them to be more efficient. Here in this work, a solar industrial process heat plant with thermal energy storage that can flexibly collect and deliver heat to two industrial processes (flexible heat integration) is dynamically modeled with control setpoints found by a dynamic optimization method. The optimized case can increase the solar efficiency of the plant by 7.5% on average relative to a base case. The results show that it is best to collect heat at lower temperatures for all but ideal solar conditions, only medium to high quality heat should be stored in large quantities, and exchanging heat with a lower temperature heat sink is generally more efficient. The optimized case is able to reduce the levelized cost of heat of the plant to $31.83/MWh th compared to a base case value of $34.10/MWh th . The optimized case can reduce emissions by 22.2% compared to a plant which uses only natural gas. This work shows that dynamic optimization with flexible heat integration can be a cost-effective way to increase efficiency so that more of these types of plants can be implemented.
A thermal receiver, such as a solar flux thermal receiver, is disclosed comprising a modular arrangement of arrayed microchannels or micropins to heat a working fluid by heat transfer. Disclosed solar receivers provide a much higher solar flux and consequently a significant reduction in thermal losses, size, and cost, relative to known receivers. Unit cell receivers can be numbered up and combined in parallel to form modules, and modules combined to form full scale receivers.
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Concentrating solar power (CSP) coupled with thermal energy storage (TES) is being considered as an appealing solution to deliver stable, dispatchable, and inexpensive electricity generation from renewable solar energy. The heat from the HTF is stored in a TES system that is significantly less expensive and more scalable than electrochemical storage, enabling electricity generation when the sunlight is unavailable. The general concentrating solar thermal technologies are being pushed to higher temperature for higher efficiency and lower cost in a diverse range application in electricity generation (in CSP), industrial heating, and thermochemical fuel productions. Inert and abundant solid particles are becoming increasingly more important for these emerging applications. Heat transfer in both stationary and moving particle beds have been studied for many decades, revealing a great deal of insights on the mechanisms. Yet, there are still several unresolved problems from both fundamental understanding and technological applications points of view. In this Chapter, we will attempt to provide an up-to-date picture of thermal transport in stationary and moving particle beds. In both cases, we will review historical and contemporary developments in the theoretical, computational, and experimental aspects of heat transfer and related behaviors in particle beds. Here, we will discuss both the advancements and limitations of the existing studies and point to future developments towards deeper fundamental understandings and better engineering applications.
Abstract Thermal transition of organic solar cells (OSCs) constituent materials are often insufficiently researched, resulting in trial‐and‐error rather than rational approaches to annealing strategies to improve domain purity to enhance the power conversion efficiency. Despite the potential utility, little is known about the thermal transitions of the modern high‐performance acceptors Y6 and N3. Here, by using an optical method, it is discovered that the acceptor N3 has a clear solid‐state aggregation transition at 82 °C. This unusually low transition not only explains prior optimization protocols, but the transition informs and enables a double‐annealing method that can fine‐tune aggregation and the device morphology. Compared with 16.6% efficiency for PM6:N3:PC 71 BM control devices, higher efficiency of 17.6% is obtained through the improved protocol. Morphology characterization with x‐ray scattering methods reveals the formation of a multilength scale morphology. Moreover, the double‐annealing method is illustrated and easily transferred and validated with Y6‐based devices, using the transition of Y6 at 102 °C. As a result, the PCE improved from 16.0% to 16.8%. Design of high‐performance acceptors with yet lower aggregation transitions might be required for OSCs to successfully transition to low thermal budget industrial processing methods where annealing temperatures on plastic substrates have to be kept low.
In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.