Flow, force, behaviour: assessment of a prototype hydraulic barrier for invasive fish

Migration barriers being selective for invasive species could protect pristine upstream areas. We designed and tested a prototype protective barrier in a vertical slot fish pass. Based on the individuals’ swimming responses to the barrier flow field, we assumed this barrier would block the ascension of the invasive round goby, but allow comparable native species (gudgeon and bullhead) to ascend. The barrier was tested in three steps: flow description, quantification of forces experienced by preserved fish in the flow field, and tracking the swimming trajectories of ca. 43 live fish per trial and species. The flow and the forces were homogenous over the barrier, though gudgeon experienced significantly smaller forces than round goby or bullhead. The swimming trajectories were distinct enough to predict the fish species with a random forest machine learning approach (92.16% accuracy for gudgeon and 85.24% for round goby). The trajectories revealed round goby and gudgeon exhibited increased, but varied, swimming speeds and straighter paths at higher water discharge. These results suggest that passage of round goby was prevented at 130 L/s water discharge, whereas gudgeon and bullhead could pass the barrier. Our findings open a new avenue of research on hydraulic constructions for species conservation.

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x; x>0) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.

Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective anode coatings possessing high ionic conductivity and good stability with Li metal has proven to be a challenge. Here, we present our informatics approach to explore the Li compound space for promising electrolytes and anode coatings using high-throughput multi-property screening and interpretable machine learning. To do this, we generate a database of battery-related materials properties by computing $$\hbox {Li}^+$$ Li + migration barriers and stability windows for over 15,000 Li-containing compounds from Materials Project. We screen through the database for candidates with good thermodynamic and electrochemical stabilities, and low $$\hbox {Li}^+$$ Li + migration barriers, identifying promising new candidates such as $$\hbox {Li}_9\hbox {S}_3$$ Li 9 S 3 N, $$\hbox {LiAlB}_2\hbox {O}_5$$ LiAlB 2 O 5 , $$\hbox {LiYO}_2$$ LiYO 2 , $$\hbox {LiSbF}_4$$ LiSbF 4 , and $$\hbox {Sr}_4\hbox {Li}(\hbox {BN}_2)_3$$ Sr 4 Li ( BN 2 ) 3 , among others. We train machine learning models, using ensemble methods, to predict migration barriers and oxidation and reduction potentials of these compounds by engineering input features that ensure accuracy and interpretability. Using only a small number of features, our gradient boosting regression models achieve $$\mathrm {R}^2$$ R 2 values of 0.95 and 0.92 on the oxidation and reduction potential prediction tasks, respectively, and 0.86 on the migration barrier prediction task. Finally, we use Shapley additive explanations and permutation feature importance analyses to interpret our machine learning predictions and identify materials properties with the largest impact on predictions in our models. We show that our approach has the potential to enable rapid discovery and design of novel solid electrolytes and anode coatings.

Descriptor and scaling relations for ion mobility in crystalline solids

Ion mobility is a critical performance parameter in electrochemical energy storage and conversion, but also in other electrochemical devices. Based on first-principles electronic structure calculations, we have derived a descriptor for the ion mobility in battery electrodes and solid electrolytes. This descriptor is entirely composed of observables that are easily accessible: ionic radii, oxidation states and the Pauling electronegativities of the involved species. Within a particular class of materials, the migration barriers are connected to this descriptor through linear scaling relations upon the variation of either the cation chemistry of the charge carriers or the anion chemistry of the host lattice. The validity of these scaling relations indicates that a purely ionic view falls short of capturing all factors influencing ion mobility in solids.The identification of these scaling relations has the potential to significantly accelerate the discovery of materials with desired mobility properties.

The role of migration barriers for dispersion of Proliferative Kidney Disease—Balance between disease emergence and habitat connectivity

Natural and uninterrupted water courses are important for biodiversity and fish population stability. Nowadays, many streams and rivers are obstructed by artificial migration barriers, often preventing the migration of fish. On the other hand, distribution of pathogens by migrating fishes is still a point of concern. Pathogen transport and transmission is a driving force in the dynamics of many infectious diseases. The aim of the study was to investigate the possible consequences of the removal of an artificial migration barrier for the upstream transport of Tetracapsuloides bryosalmonae, the causative agent of Proliferative Kidney Disease (PKD) in brown trout, by migrating fish. To test this question, a river system was selected with a migration barrier separating a PKD positive river from a PKD negative tributary. After removal of the barrier, PKD prevalence and pathology was examined during five years after elimination of the barrier. In the tributary, no PKD was recorded at any time of the survey. By means of unidirectional PIT (passive integrated transponder)-tagging, we confirmed upstream migration of adult brown trout into the tributary during the cold season, presumably for spawning. By eDNA, we confirmed presence of T. bryoalmonae and Fredericella sp., the definitive host, DNA in water from the PKD positive river stretch, but not in the PKD negative tributary. Our study illustrates the importance of the connectivity of streams for habitat maintenance. Although migration of brown trout from a PKD-positive river into a PKD-negative tributary, mainly for spawning, was confirmed, upstream spreading of PKD was not observed.

Leaky barriers: leaky enough for fish to pass?

Perceived as environmental-friendly hydraulic structures, leaky barriers used for natural flood management are introduced into rivers, potentially creating migration barriers for fish. Using sustainable, local materials to construct wooden barriers across river channels in upper catchments, these barriers aim to slow down the flow, reduce flood peaks and attenuate the flow reaching downstream communities. Yet little is known about their impact on hydrodynamics and fish passage. Here, we examined two model barrier designs under 100% and 80% bankfull flow conditions in an open channel flume. These barriers included a porous and a non-porous design, with the latter emulating the natural accumulation of brush, sediment and leaf material between logs over time. Flow visualization and velocity measurements recorded with acoustic Doppler velocimetry characterized the flow field upstream and downstream of the barriers. Our fish behavioural studies revealed that juvenile salmon ( Salmo salar ) movement between downstream and upstream sections of the flume was inhibited by barrier design rather than discharge, influencing upstream fish passage and their spatial preference, indicating the importance of barrier design criteria to facilitate fish movement.

Expanding the family of mineral-like anhydrous alkali copper sulfate framework structures: new phases, topological analysis and evaluation of ion migration potentialities

Two novel compounds, K2Cu3(SO4)4 and KNaCu(SO4)2, were synthesized. The crystal structure of K2Cu3(SO4)4 is based on a [Cu3(SO4)4]2− framework with relatively simple bond topology, but with four different CuO n polyhedron geometries. The K+ cations reside in the pores of the framework. The [Cu(SO4)2]2− framework in KNaCu(SO4)2 encloses large elliptical channels running along [001]. Larger channels are occupied by K+, whereas smaller ones are filled by Na+. The bond-valence energy landscape (BVEL) approach has been demonstrated to be a useful method for the prediction of the mobility of alkali metal ions in various structures. By means of this approach, the threshold energies at which isosurfaces begin to percolate as well as the directions of possible ion migration in the structures were determined. The modelling of ion migration maps by the analysis of the procrystal electron-density distribution was used to rapidly identify ion migration pathways and limiting barriers between particular crystallographic sites in the structures under consideration. Its consistency and complementarity with the BVEL method have been demonstrated. Both approaches revealed a relatively low ion threshold percolation and migration barriers in the cryptochalcite-type structures [cryptochalcite: K2Cu5O(SO4)5]. Hence, one may assume that its 3D framework type is suited for ion transport applications. The review of all known members of the groups of anhydrous copper sulfates did not reveal a correlation between the porosity of the framework structures and a manifestation of ion conduction properties.

Hydride-based antiperovskites with soft anionic sublattices as fast alkali ionic conductors

Most solid-state materials are composed of p-block anions, only in recent years the introduction of hydride anions (1s2) in oxides (e.g., SrVO2H, BaTi(O,H)3) has allowed the discovery of various interesting properties. Here we exploit the large polarizability of hydride anions (H–) together with chalcogenide (Ch2–) anions to construct a family of antiperovskites with soft anionic sublattices. The M3HCh antiperovskites (M = Li, Na) adopt the ideal cubic structure except orthorhombic Na3HS, despite the large variation in sizes of M and Ch. This unconventional robustness of cubic phase mainly originates from the large size-flexibility of the H– anion. Theoretical and experimental studies reveal low migration barriers for Li+/Na+ transport and high ionic conductivity, possibly promoted by a soft phonon mode associated with the rotational motion of HM6 octahedra in their cubic forms. Aliovalent substitution to create vacancies has further enhanced ionic conductivities of this series of antiperovskites, resulting in Na2.9H(Se0.9I0.1) achieving a high conductivity of ~1 × 10–4 S/cm (100 °C).