{"id":740,"date":"2021-11-12T15:33:29","date_gmt":"2021-11-12T14:33:29","guid":{"rendered":"https:\/\/compbat.eu\/?p=740"},"modified":"2021-11-12T15:38:56","modified_gmt":"2021-11-12T14:38:56","slug":"the-infographic-column-unipi","status":"publish","type":"post","link":"https:\/\/compbat.eu\/2021\/11\/12\/the-infographic-column-unipi\/","title":{"rendered":"The infographic column: Unipi"},"content":{"rendered":"

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The electrical energy storage is one of the key challenges to address in order to realize the green transition to low-carbon economy. An extensive storage capacity will be required to balance the fluctuating nature of variable renewable energy sources such as wind and solar.<\/span><\/p>\n

Redox Flow Batteries (RFBs) are a promising option for large scale stationary energy storage. RFBs utilize\u00a0 redox active materials dissolved in liquid electrolytes stored in tanks, and the amount of the stored energy depends on the volume of the tanks. Electrolytes are circulated through electrochemical cells where the electrochemical reactions take place.\u00a0
<\/span>RFBs have a great potential for stationary applications, due to a set of features such as the possibility to decouple energy (tank size and amount of electrolyte) and power (cell surface and pumped mass flow) thanks to their modular nature which makes them extremely flexible and scalable in design. In addition, it is possible to fully recover performance losses with time and number of cycles, up to nominal conditions with a moderate maintenance and finally the lifecycle that they can reach is long, above 10000 cycles which may easily mean ten to twenty years thanks to the already reached stability of the redox active materials.<\/span><\/p>\n

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State-of-the-art of RFBs work with non-flammable aqueous electrolytes utilizing highly acidic solutions based on vanadium sulfates. On the one hand, vanadium is recyclable and not scarcely present on earth as lithium, therefore if properly recycled it would lead to a lower environmental footprint. On the other hand, it would be better to develop the RFBs working with non-corrosive solutions and based on ubiquitous materials such as organic molecules or non-toxic metals. This could boost a large scale deployment because it would dramatical reduce its price and environmental impact, plus reducing the dependence of European Union from importing vanadium.<\/span><\/p>\n

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Affordable RFBs based on renewable or abundant raw materials are therefore urgently needed. The potential energy storage materials are chemicals that need to fulfil several criteria, including a reasonable high energy density, stability, and production at an affordable cost, but no such chemicals have been discovered yet. To contribute towards solving this problem, CompBat will focus on developing tools for discovery of new prospective candidates for next generation flow batteries, based on machine learning assisted high-throughput screening. Density functional theory calculations will be used to obtain data on solubilities and redox potentials of different molecules, and machine learning methods are used to develop high-throughput screening tools based on the obtained data. The results of the high-throughput screening are validated with experimental results. Target molecules will be bio-inspired organic compounds, as well as derivatives of the redox active specialty chemicals already manufactured in bulk quantities. We propose to use safe and inexpensive natural products, such as vitamins and amino acids as building blocks for aqueous flow battery materials operating close to neutral pH. The advantages of natural product derived materials include: 1) scalable production in tanks by fermentation with reasonable cost, 2) inherent safety and expected biodegradability due to their biological origin and natural roles even in the human body, 3) solubility in water, and 4) high degree of functionalization, minimizing the need for synthetic steps to modify them.<\/span><\/p>\n

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As next step, numerical modelling of flow battery systems will be performed with finite element method enabling to investigate cell details and provide the required information to develop more general zero-dimensional models based on mass-transfer coefficients. The latter would enable faster and reliable simulations of the cells performance that could be verified with the first ones.\u00a0\u00a0Both models will also be validated experimentally, and used for generating a data-set to allow prediction of the flow battery cell performance based on properties of the prospective candidates obtained from high throughput screening.<\/span><\/p>\n

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Finally, cells will be piled in stacks reaching higher power values and modelled to understand the effect of shunt parasitic currents, as well as including the effect of other components as pumps and tanks. Furthermore the battery performance will be enhanced by the use of solid boosters, to be manufactured from inexpensive and abundant raw materials. Solid boosters consist of particles introduced into the flow battery solution tanks as beads composed only of the active material, conductive additive and binder. Due to the reaction between the solid particles and the active components present in the electrolyte, the dissolved redox species, the storage capacity will be enhanced only at the extra cost of the raw materials. In fact the amount of electrolyte will be the same and the limiting factor to the capacity will no longer be the solubility of the dissolved species determining a higher energy density than that of the conventional RFBs.<\/span><\/p>\n

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This data is used then to predict the flow battery overall system performance. In this way including economic assumptions will be possible to develop tools capable to assess also the battery costs, identifying parameters such the Levelized Cost Of Storage (LCOS).<\/span><\/p>\n

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The electrical energy storage is one of the key challenges to address in order to realize the green transition to low-carbon economy. An extensive storage capacity will be required to balance the fluctuating nature of variable renewable energy sources such as wind and solar. Redox Flow Batteries (RFBs) are a promising option for large scale […]<\/p>\n","protected":false},"author":6,"featured_media":746,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"

The TTK research team is involved in developing high-throughput screening (HTS) methods that enables the identification of promising candidates of water-soluble redox-active compounds for experimental synthesis and electrochemical characterization. The focus is on bioinspired molecules derived from vitamins and amino acids, which are promising candidate compounds for novel redox flow batteries (RFBs). Our general strategy along these lines is to build an initial database for two basic quantities relevant to RFBs (redox potentials and aqueous solubilities), and utilize various machine learning techniques to provide efficient screening methodology applicable for a large and diverse set of molecules.<\/span><\/p>

\u00a0<\/p>

Molecular design<\/b> via systematic variation of functional groups is the initial step in our approach. The molecular database is built in a combinatorial manner: We define various molecular frameworks and introduce substituents with broadly varying electronic and steric properties. In our developed protocol, we first generate the 3D structures of the molecules, which is followed by an extensive conformational analysis to find the energetically most favored molecular structures.<\/span><\/p>

\u00a0<\/p>

The redox potentials and aqueous solubilities are computed via an efficient composite protocol that employs a combination of various modelling tools ranging from simple force-field methods to advanced quantum chemical calculations, but also including semiempirical computational methods. Accurate <\/span>quantum chemical calculations<\/b> represent the computationally most demanding step of our protocol. These calculations are carried out using density functional theory (DFT) and we use high-performance computer facilities to obtain accurate electronic structure data.<\/span><\/p>

\u00a0<\/p>

The developed molecular database is used to train and validate various <\/span>machine learning<\/b> approaches in the next phase of the project. The molecular library is then expanded iteratively in terms of the number of molecules, as well as the diversity of molecular frameworks. This procedure will thus result in a <\/span>HTS tool<\/b> that is applicable to a large set of redox-active compounds and will assist the discovery of new prospective candidates for next generation flow batteries.<\/span><\/p>

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