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Proteins are essential macromolecules defined by their amino acid sequences, which determine their three-dimensional structures and, consequently, their functions in all living organisms. Therefore, generative protein modeling necessitates a multimodal approach to simultaneously model, understand, and generate both sequences and structures. However, existing methods typically use separate models for each modality, limiting their ability to capture the intricate relationships between sequence and structure. This results in suboptimal performance in tasks that requires joint understanding and generation of both modalities. In this paper, we introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. To enable structural learning with the language model, 3D coordinates are converted to discrete tokens using a lookup-free quantization-based tokenizer. By training on both experimental and high-quality synthetic structures, DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. We also implement an efficient warm-up strategy to exploit the connection between large-scale evolutionary data and structural inductive biases from pre-trained sequence-based protein language models. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures eliminating the need for a two-stage generation approach. Moreover, DPLM-2 demonstrates competitive performance in various conditional generation tasks, including folding, inverse folding, and scaffolding with multimodal motif inputs, as well as providing structure-aware representations for predictive tasks.

Developing strategies to systematically increase the critical current, the threshold current below which the superconductivity exists, is an important goal of materials science. Here, the concept of quantum phase transition is employed to enhance the critical current of a kagome superconductor CsV$_3$Sb$_5$, which exhibits a charge density wave (CDW) and superconductivity that are both affected by hydrostatic pressure. As the CDW phase is rapidly suppressed under pressure, a large enhancement in the self-field critical current ($I_{\rm c,sf}$) is recorded. The observation of a peak-like enhancement of $I_{\rm c,sf}$ at the zero-temperature limit ($I_{\rm c,sf}(0)$) centred at $p^*\approx 20$~kbar, the same pressure where the CDW phase transition vanishes, further provides strong evidence of a zero-temperature quantum anomaly in this class of pressure-tuned superconductor. Such a peak in $I_{\rm c,sf}(0)$ resembles the findings in other well-established quantum-critical superconductors, hinting at the presence of enhanced quantum fluctuations associated with the CDW phase in CsV$_3$Sb$_5$., Comment: 8 pages, 4 figures. Advanced Science (2024)

Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics. However, the capabilities and limitations associated with these models remain poorly understood due to the absence of a unified evaluation framework. To fill this gap, we introduce ProteinBench, a holistic evaluation framework designed to enhance the transparency of protein foundation models. Our approach consists of three key components: (i) A taxonomic classification of tasks that broadly encompass the main challenges in the protein domain, based on the relationships between different protein modalities; (ii) A multi-metric evaluation approach that assesses performance across four key dimensions: quality, novelty, diversity, and robustness; and (iii) In-depth analyses from various user objectives, providing a holistic view of model performance. Our comprehensive evaluation of protein foundation models reveals several key findings that shed light on their current capabilities and limitations. To promote transparency and facilitate further research, we release the evaluation dataset, code, and a public leaderboard publicly for further analysis and a general modular toolkit. We intend for ProteinBench to be a living benchmark for establishing a standardized, in-depth evaluation framework for protein foundation models, driving their development and application while fostering collaboration within the field., Comment: 30 pages, 2 figures and 15 tables

This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. We first pre-train scalable DPLMs from evolutionary-scale protein sequences within a generative self-supervised discrete diffusion probabilistic framework, which generalizes language modeling for proteins in a principled way. After pre-training, DPLM exhibits the ability to generate structurally plausible, novel, and diverse protein sequences for unconditional generation. We further demonstrate the proposed diffusion generative pre-training makes DPLM possess a better understanding of proteins, making it a superior representation learner, which can be fine-tuned for various predictive tasks, comparing favorably to ESM2 (Lin et al., 2022). Moreover, DPLM can be tailored for various needs, which showcases its prowess of conditional generation in several ways: (1) conditioning on partial peptide sequences, e.g., generating scaffolds for functional motifs with high success rate; (2) incorporating other modalities as conditioner, e.g., structure-conditioned generation for inverse folding; and (3) steering sequence generation towards desired properties, e.g., satisfying specified secondary structures, through a plug-and-play classifier guidance. Code is released at \url{https://github.com/bytedance/dplm}., Comment: ICML 2024 camera-ready version

The interplay between dimensionality and various phases of matter is a central inquiry in condensed matter physics. New phases are often discovered through spontaneously broken symmetry. Understanding the dimensionality of superconductivity in the high-temperature cuprate analogue $-$ layered nickelates and revealing a new symmetry-breaking state are the keys to deciphering the underlying pairing mechanism. Here, we demonstrate the highly-tunable dimensionality and a broken rotational symmetry state in the superconductivity of square-planar layered nickelates. The superconducting state, probed by superconducting critical current and magnetoresistance within superconducting transition under direction-dependent in-plane magnetic fields, exhibits a $C_2$ rotational symmetry which breaks the $C_4$ rotational symmetry of the square-planar lattice. Furthermore, by performing detailed examination of the angular dependent upper critical fields at temperatures down to 0.5 K with high-magnetic pulsed-fields, we observe a crossover from two-dimensional to three-dimensional superconducting states which can be manipulated by the ionic size fluctuations in the rare-earth spacer layer. Such a large degree of controllability is desired for tailoring strongly two/three-dimensional superconductors and navigating various pairing landscapes for a better understanding of the correlation between reduced dimensionality and unconventional pairing. These results illuminate new directions to unravel the high-temperature superconducting pairing mechanism., Comment: 30 pages, 4 main figures, 8 extended data figures

The kagome metal CsV$_{3}$Sb$_{5}$ features an unusual competition between the charge-density-wave (CDW) order and superconductivity. Evidence for time-reversal symmetry breaking (TRSB) inside the CDW phase has been accumulating. Hence, the superconductivity in CsV$_{3}$Sb$_{5}$ emerges from a TRSB normal state, potentially resulting in an exotic superconducting state. To reveal the pairing symmetry, we first investigate the effect of nonmagnetic impurity. Our results show that the superconducting critical temperature is insensitive to disorder, pointing to conventional $s$-wave superconductivity. Moreover, our measurements of the self-field critical current ($I_{c,sf}$), which is related to the London penetration depth, also confirm conventional $s$-wave superconductivity with strong coupling. Finally, we measure $I_{c,sf}$ where the CDW order is removed by pressure and superconductivity emerges from the pristine normal state. Our results show that $s$-wave gap symmetry is retained, providing strong evidence for the presence of conventional $s$-wave superconductivity in CsV$_{3}$Sb$_{5}$ irrespective of the presence of the TRSB, Comment: 8 pages, 4 figures. Nano Letters (in press)

Recently, non-autoregressive (NAR) neural machine translation models have received increasing attention due to their efficient parallel decoding. However, the probabilistic framework of NAR models necessitates conditional independence assumption on target sequences, falling short of characterizing human language data. This drawback results in less informative learning signals for NAR models under conventional MLE training, thereby yielding unsatisfactory accuracy compared to their autoregressive (AR) counterparts. In this paper, we propose a simple and model-agnostic multi-task learning framework to provide more informative learning signals. During training stage, we introduce a set of sufficiently weak AR decoders that solely rely on the information provided by NAR decoder to make prediction, forcing the NAR decoder to become stronger or else it will be unable to support its weak AR partners. Experiments on WMT and IWSLT datasets show that our approach can consistently improve accuracy of multiple NAR baselines without adding any additional decoding overhead., Comment: Accepted by EMNLP 2022

(Ca$_{x}$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$ is a rare system that has been shown to display an interesting interplay between structural quantum criticality and superconductivity. A putative structural quantum critical point, which is hidden beneath a broad superconducting dome, is believed to give rise to optimized superconducting properties in (Ca$_{x}$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$. However, the presence of the superconducting dome itself hinders the examination of the quantum critical point through electrical transport, as the transport coefficients vanish in the superconducting state. Here, we use critical current density to explore within the superconducting dome. Our measurements reveal a large enhancement of the critical current density at the zero-temperature limit when the system is tuned towards the structural quantum critical point., Comment: 7 pages, 4 figures

Kagome metals AV$_3$Sb$_5$~(A = K, Rb, Cs) are recently discovered platforms featuring an unusual charge-density-wave (CDW) order and superconductivity. The electronic band structure of a kagome lattice can host both flat bands as well as Dirac-like bands, offering the possibility to stabilize various quantum states. Here, we probe the band structure of CsV$_3$Sb$_5$ via Shubnikov-de Haas quantum oscillations on both bulk single crystals and thin flakes. Although our frequency spectra are broadly consistent with the published data, we unambiguously reveal the existence of new frequencies with large frequencies ranging from $\sim$2085~T to $\sim$2717~T in thin flakes when the magnetic field is along the $c$-axis. These quasi-two-dimensional frequencies correspond to $\sim$52\% to 67\% of the CDW-distorted Brillouin zone volume. The Lifshitz-Kosevich analysis further uncovers surprisingly small cyclotron effective masses, of the order of $\sim$0.1~$m_e$, for these frequencies. Consequently, a large number of high-velocity carriers exists in the thin flake of CsV$_3$Sb$_5$. Comparing with our band structure calculations, we argue that an orbital-selective modification of the band structure is active. Our results provide indispensable information for understanding the fermiology of CsV$_3$Sb$_5$, paving a way for understanding how an orbital-selective mechanism can become an effective means to tune its electronic properties., Comment: 6 figures. To appear in Phys. Rev. B

Multi-agent formation as well as obstacle avoidance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control achieve a certain measure of success, most of them require precise global information which is not accessible in harsh environments. On the other hand, some reinforcement learning (RL) based approaches adopt the leader-follower structure to organize different agents' behaviors, which sacrifices the collaboration between agents thus suffering from bottlenecks in maneuverability and robustness. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). Agents in our system only utilize local and relative information to make decisions and control themselves distributively. Agent in the multi-agent system will reorganize themselves into a new topology quickly in case that any of them is disconnected. Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines (both classic control methods and another RL-based method). The feasibility of our method is verified by both simulation and hardware implementation with Ackermann-steering vehicles.

The emergence of high transition temperature ($T_c$) superconductivity in bulk FeSe under pressure is associated with the tuning of nematicity and magnetism. However, sorting out the relative contributions from magnetic and nematic fluctuations to the enhancement of $T_c$ remains challenging. Here, we design and conduct a series of high-pressure experiments on FeSe thin flakes. We find that, as the thickness decreases, the nematic phase boundary on temperature-pressure phase diagrams remains robust while the magnetic order is significantly weakened. A local maximum of $T_c$ is observed outside the nematic phase region, not far from the extrapolated nematic endpoint in all samples. However, the maximum $T_c$ value is reduced associated with the weakening of magnetism. No high-$T_c$ phase is observed in the thinnest sample. Our results strongly suggest that nematic fluctuations alone can only have a limited effect while magnetic fluctuations are pivotal on the enhancement of $T_c$ in FeSe., Comment: 8 pages, 5 figures. This is the first draft of an accepted paper by Nano Letters

Charge density wave (CDW) instability is often found in phase diagrams of superconductors such as cuprates and certain transition-metal dichalcogenides. This proximity to superconductivity triggers the question on whether CDW instability is responsible for the pairing of electrons in these superconductors. However, this issue remains unclear and new systems are desired to provide a better picture. Here, we report the temperature-pressure phase diagram of a recently discovered BiS$_2$ superconductor La$_2$O$_2$Bi$_3$AgS$_6$, which shows a possible CDW transition at $T^*\sim$155 K and a superconducting transition at $T_c\sim$1.0 K at ambient pressure, via electrical resistivity measurements. Upon applying pressure, $T^*$ decreases linearly and extrapolates to 0 K at 3.9 GPa. Meanwhile, $T_c$ is enhanced and reaches maximum value of 4.1 K at 3.1 GPa, forming a superconducting dome in the temperature-pressure phase diagram., Comment: 8 pages, 4 figures. This is the first draft of an accepted paper by J. Phys. Condens. Matter

Formation and collision avoidance abilities are essential for multi-agent systems. Conventional methods usually require a central controller and global information to achieve collaboration, which is impractical in an unknown environment. In this paper, we propose a deep reinforcement learning (DRL) based distributed formation control scheme for autonomous vehicles. A modified stream-based obstacle avoidance method is applied to smoothen the optimal trajectory, and onboard sensors such as Lidar and antenna arrays are used to obtain local relative distance and angle information. The proposed scheme obtains a scalable distributed control policy which jointly optimizes formation tracking error and average collision rate with local observations. Simulation results demonstrate that our method outperforms two other state-of-the-art algorithms on maintaining formation and collision avoidance., Comment: 5 pages, 5 figures, been accepted and to be published in IEEE International Conference on Autonomous Systems 2021

Autonomous electric vehicles are being widely studied nowadays as the future technology of ground transportation, while the autonomous electric vehicles based on conventional powertrain system limit their energy and power transmission efficiencies and may hinder their broad applications in future. Here we report a study on the energy consumption and efficiency improvement of a mid-size autonomous electric vehicle driven by in-wheel motors, through the development of a numerical energy model, validated with the actual driving data and implemented in a case study. The energy analysis was conducted under three driving conditions: flat road, upslope, and downslope driving to examine the energy consumption, with the energy-saving potential of the in-wheel-motor driven powertrain system systematically explored and discussed. Considering the energy recovery from the regenerative braking, energy consumption and regenerated energy were calculated in specific driving cycles based on vehicle dynamics and autonomous driving patterns. A case study was conducted using the baseline electric vehicle driving data in West Los Angeles. It was found that an in-wheel motor driven autonomous electric vehicle can save up to 17.5% of energy compared with a conventional electric vehicle during the slope driving. Using the efficiency maps of a commercial in-wheel motor, the numerical energy model and validated results obtained from this study are in line with actual situations, and can be used to support sustainable development of more energy-efficient autonomous electric vehicles in the future.

Li-ion batteries with nickel-manganese-cobalt (NMC) cathode and graphite anode are popularly used in portable electronic devices and electric vehicles. Calendar loss of the lithium ion battery is a dominating factor in battery degradation. However, few modeling work was reported on studying the calendar capacity loss of NMC-graphite Li-ion batteries. In this work, an electrochemical model for NMC-graphite Li-ion battery was developed to investigate its calendar loss behavior. Various factors affecting the calendar loss of the NMC-graphite batteries were systematically investigated, with the results validated with experimental data of a Sanyo 18,650 cylindrical cell. It was found that at 25 o C working temperature and 100% SOC, the capacity drops 6.4% of its original capacity after 10 months. Also, when the anode particle size decreases from 26.2 {\mu}m to 6.55 {\mu}m, the capacity drop ratio is over 22% after 10 months under the same operation condition. Our simulation results demonstrate that a smaller SOC, a lower cell working temperature and a larger particle size could prolong the battery life during the storage period. This modeling work can help better understand the calendar loss behavior of NMC-graphite Li-ion batteries, and serve as a robust reference for the battery performance optimization in future.

Hydrated excess protons under hydrophobic confinement are a critical component of charge transport behavior and reactivity in nanoporous materials and biomolecular systems. Herein excess proton confinement effects are computationally investigated for sub-2 nm hydrophobic nanopores by varying the diameters (d = 0.81, 0.95, 1.09, 1.22, 1.36, 1.63, and 1.90 nm), lengths (l ~3 and ~5 nm), curvature, and chirality of cylindrical carbon nanotube (CNT) nanopores. CNTs with a combination of different diameter segments are also explored. The spatial distribution of water molecules under confinement are diameter-dependent; however, proton solvation and transport is consistently found to occur in the water layer adjacent to the pore wall, showing an "amphiphilic" character of the hydrated excess proton hydronium-like structure. The proton transport free energy barrier also decreases significantly as the nanopore diameter increases and proton transport becomes almost barrierless in the d > 1 nm nanopores. Among the nanopores studied, the Zundel cation (${H_{5}O_{2}}^{+}$) is populated only in the d = 0.95 nm CNT (7,7) nanopore. The presence of the hydrated excess proton and $K^{+}$ inside the CNT (7,7) nanopore induces a water density increase by 40 and 20%, respectively. The $K^{+}$ transport through CNT nanopores is also consistently higher in free energy barrier than proton transport. Interestingly, the evolution of excess protonic charge defect distribution reveals a "frozen" single water wire configuration in the d = 0.81 nm CNT (6,6) nanopore (or segment), through which hydrated excess protons can only shuttle via the Grotthuss mechanism. Vehicular diffusion becomes relevant to proton transport in the "flat" free energy regions and in the wider nanopores, where protons do not primarily shuttle in the axial direction., Comment: 54 pages, 15 figures

Whilst electron correlations were previously recognized to trigger beyond conventional direct current (DC) electronic transportations (e.g. metal-to-insulator transitions, bad metal, thermistors), their respective influences to the alternation current (AC) transport are largely overlooked. Herein, we demonstrate active regulations in the electronic functionalities of d-band correlated rare-earth nickelate (ReNiO3) thin films, by simply utilizing their electronic responses to AC-frequencies (fAC). Assisted by temperature dependent near edge X-ray absorption fine structure analysis, we discovered positive temperature dependences in Coulomb viscosity of ReNiO3 that moderates their AC impedance. Distinguished crosslinking among R(Real)-fAC measured in nearby temperatures is observed that differs to conventional oxides. It enables active adjustability in correlated transports of ReNiO3, among NTCR-, TDelta- and PTCR- thermistors, via fAC from the electronic perspective without varying materials or device structures. The TDelta-fAC relationship can be further widely adjusted via Re composition and interfacial strains. The AC-frequency sensitivity discovered in ReNiO3 brings in a new freedom to regulating and switching the device working states beyond the present semiconductor technologies. It opens a new paradigm for enriching novel electronic applications catering automatic transmission or artificial intelligence in sensing temperatures and frequencies.

To date, investigations of carrier-envelope-phase (CEP) dependent effects have been limited to optical pulses with few cycles and high intensity, and have not been reported for other types of pulses. Optomechanical systems are shown to have the potential to go beyond these limits. We present an approach using optomechanics to extend the concept of the traditional CEP in the few-cycle regime to mechanical pulses and develop a two-step model to give a physical insight. By adding an auxiliary continuous optical field, we show that a CEP-dependent effect appears even in the multi-cycle regime of mechanical pulses. We obtain the approximated analytical solutions providing full understanding for these optomechanically induced CEP-dependent effects. In addition, our findings show that one can draw on the optomechanical interaction to revive the CEP-dependent effects on optical pulses with an arbitrary number of cycles and without specific intensity requirements. The effects of CEP, broadly extended to encompass few- and multi-cycle optical and mechanical pulses, may stimulate a variety of applications in the preparation of a CEP-stabilized pulse, the generation of ultrasonic pulses with a desired shape, the linear manipulation of optical combs, and more., Comment: 8 pages, 3 figures

Anisotropies in electronic transportations conventionally originate from the nature of low symmetries in crystal structures, and were not anticipated for perovskite oxides, the crystal asymmetricity of which is far below, e.g. van der Waals or topological crystal. Beyond conventional expectations, herein we demonstrate pronounced anisotropies in the inter-band coulomb repulsion dominated electronic transportation behaviors under low-dimensional confinement for the perovskite family of rare-earth nickelates (ReNiO3). From one aspect, imparting bi-axial interfacial strains upon various lattice planes results in extrinsic anisotropies in the abrupt orbital transitions of ReNiO3, and their metal to insulator transition behaviors that elevates the transition temperature beyond the existing merit. From the other aspect, the in-plane orbital entropy associated to the in-plane symmetry of the NiO6 octahedron within ReNiO3 causes intrinsic anisotropies for the gradually orbital transition with temperature to further improve their thermistor transportation properties. The present work unveils the overlooked role of the electronic orbital directionality within low dimensional correlated perovskites that can trigger anisotropic transportation behaviors, in spite of their relatively symmetric crystal structures. Establishing anisotropic transportations integrating the electron correlation and quantum confinement effects will bring in a new freedom for achieving further improvement in transportation properties of multi-functional perovskite oxides.

Establishing strong electron-correlations not only shed lights on overcoming the trade-off limitations for optimizing thermoelectric materials, but can also introduce new functionalities that extend the vision of conventional thermoelectric applications. Here, we demonstrate that the high thermoelectric and thermistor functionalities coexist in lattice distorted SrNbxTi1-xO3 films with electron correlations between carriers and ordering aligned lattice dipoles. As-grown SrNbxTi1-xO3/SrTiO3 with effectively preserved interfacial strains exhibits cross-plane charge ordering and orbital anisotropy, as indicated by the polarization dependent near edge X-ray absorption fine structures. The resultant coulomb-correlations regulate the carrier transport and enhance the Seebeck coefficient more independently via enlarging the system vibration entropy. As-achieved maximum thermoelectric power factor exceeds 100 uWcm-1K-2 measured in the bulk performance of SrNb0.2Ti0.8O3 (2.2 um)/SrTiO3 (100 um), which is comparable to the best thermoelectric materials for low temperature applications. In addition, the strong temperature dependence of the carrier scattering aroused by the lattice dipoles introduces a positive temperature dependent thermistor transportation behavior with large temperature coefficient of resistance ranging from 30 to 300 K, which is rarely seen in conventional thermistors. Combining both functionalities largely extend the horizon in exploring new Joule sensors for detection of temperature and thermal perturbations across a broad temperature range.

Rechargeable redox flow batteries with serpentine flow field designs have been demonstrated to deliver higher current density and power density in medium and large-scale stationary energy storage applications. Nevertheless, the fundamental mechanisms involved with improved current density in flow batteries with flow field designs have not been understood. Here we report a maximum current density concept associated with stoichiometric availability of electrolyte reactant flow penetration through the porous electrode that can be achieved in a flow battery system with a "zero-gap"serpentine flow field architecture. This concept can explain a higher current density achieved within allowing reactions of all species soluble in the electrolyte. Further validations with experimental data are confirmed by an example of a vanadium flow battery with a serpentine flow structure over carbon paper electrode.

Thermoelectric (TE) materials achieve localised conversion between thermal and electric energies, and the conversion efficiency is determined by a figure of merit zT. Up to date, two-dimensional electron gas (2DEG) related TE materials hold the records for zT near room-temperature. A sharp increase in zT up to ~2.0 was observed previously for superlattice materials such as PbSeTe, Bi2Te3/Sb2Te3 and SrNb0.2Ti0.8O3/SrTiO3, when the thicknesses of these TE materials were spatially confine within sub-nanometre scale. The two-dimensional confinement of carriers enlarges the density of states near the Fermi energy3-6 and triggers electron phonon coupling. This overcomes the conventional {\sigma}-S trade-off to more independently improve S, and thereby further increases thermoelectric power factors (PF=S2{\sigma}). Nevertheless, practical applications of the present 2DEG materials for high power energy conversions are impeded by the prerequisite of spatial confinement, as the amount of TE material is insufficient. Here, we report similar TE properties to 2DEGs but achieved in SrNb0.2Ti0.8O3 films with thickness within sub-micrometer scale by regulating interfacial and lattice polarizations. High power factor (up to 103 {\mu}Wcm-1K-2) and zT value (up to 1.6) were observed for the film materials near room-temperature and below. Even reckon in the thickness of the substrate, an integrated power factor of both film and substrate approaching to be 102 {\mu}Wcm-1K-2 was achieved in a 2 {\mu}m-thick SrNb0.2Ti0.8O3 film grown on a 100 {\mu}m-thick SrTiO3 substrate. The dependence of high TE performances on size-confinement is reduced by ~103 compared to the conventional 2DEG-related TE materials. As-grown oxide films are less toxic and not dependent on large amounts of heavy elements, potentially paving the way towards applications in localised refrigeration and electric power generations.

In this work, a two-dimensional mathematical model is developed to study the flow patterns and volumetric flow penetrations in the flow channel over the porous electrode layered system in vanadium flow battery with serpentine flow field design. The flow distributions at the interface between the flow channel and porous electrode are examined. It is found that the non-linear pressure distributions can distinguish the interface flow distributions under the ideal plug flow and ideal parabolic flow inlet boundary conditions. However, the volumetric flow penetration within the porous electrode beneath the flow channel through the integration of interface flow velocity reveals that this value is identical under both ideal plug flow and ideal parabolic flow inlet boundary conditions. The volumetric flow penetrations under the advection effects of flow channel and landing/rib are estimated. The maximum current density achieved in the flow battery can be predicted based on the 100% amount of electrolyte flow reactant consumption through the porous electrode beneath both flow channel and landing channel. The corresponding theoretical maximum current densities achieved in vanadium flow battery with one and three layers of SGL 10AA carbon paper electrode have reasonable agreement with experimental results under a proper permeability., Comment: 36 pages, 9 figures, 1 table

A simple analytical model of a layered system comprised of a single passage of a serpentine flow channel and a parallel underlying porous electrode (or porous layer) is proposed. This analytical model is derived from Navier-Stokes motion in the flow channel and Darcy-Brinkman model in the porous layer. The continuities of flow velocity and normal stress are applied at the interface between the flow channel and the porous layer. The effects of the inlet volumetric flow rate, thickness of the flow channel and thickness of a typical carbon fiber paper porous layer on the volumetric flow rate within this porous layer are studied. The maximum current density based on the electrolyte volumetric flow rate is predicted, and found to be consistent with reported numerical simulation. It is found that, for a mean inlet flow velocity of 33.3 cm s-1, the analytical maximum current density is estimated to be 377 mA cm-2, which compares favorably with experimental result reported by others of ~400 mA cm-2., Comment: 20 pages, 5 figures, 1 table

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.