Data-centric materials informatics has become a transformative paradigm for accelerating the discovery and design of superalloys, particularly by enabling efficient prediction of properties that are experimentally inaccessible or computationally intractable due to constraints in cost, time, or complexity. By harnessing the ability of machine learning (ML) to model complex, nonlinear, and high-dimensional relationships, this approach provides a compelling alternative to traditional trial-and-error and simulation-based strategies. This review presents a comprehensive and critical assessment of recent advances in ML for superalloys. We first delineate the essential workflow for ML-enabled superalloy design, encompassing foundational data resources, quantitative assessments of data quality, feature descriptors and feature-selection strategies, representative algorithms tailored to small and heterogeneous datasets, rigorous model-evaluation protocols, and model interpretation through explainable ML and symbolic regression. We then summarize state-of-the-art ML applications targeting specific high-temperature performance metrics, particularly γ' phase stability, creep behavior, fatigue life, and oxidation resistance, and highlight how approaches such as multi-fidelity learning, data augmentation, transfer learning, and optimization algorithms facilitate efficient exploration of vast composition-processing design spaces. Finally, we discuss persisting challenges and emerging opportunities, including data scarcity and reliability, model confidence and uncertainty quantification, cross-system generalizability across Co-, Ni-, and multi-principal superalloys, high-dimensional multi-objective optimization, and the integration of physics-informed models and large language models into materials-informatics workflows. By synthesizing these developments, this review outlines a strategic roadmap for harnessing ML to accelerate the discovery, performance optimization, and intelligent design of next-generation superalloys.

Mineral processing wastewater poses severe environmental risks due to its complex composition (high suspended solids, residual reagents, heavy metals), making its treatment critical for sustainable mining. This review systematically summarizes mineral processing wastewater treatment technologies, including conventional methods and emerging approaches. Conventional physical-chemical methods are widely used but suffer from sludge production and limited resource recovery. Advanced oxidation processes (e.g., plasma oxidation, photo-Fenton) efficiently degrade refractory organics and novel adsorbents (MOFs, selective resins) enable targeted heavy metal recovery and deep purification. Artificial intelligence and digital twin further promote intelligent process control. Future directions focus on integrating multi-technologies into "classification treatment-quality-based reuse" systems to achieve comprehensive recovery of water, salts, and valuable metals, advancing mining towards a circular economy and near-zero discharge.

Proton exchange membrane water electrolysis (PEMWE) has long been regarded as a promising technology for hydrogen production due to its high electrolytic efficiency, reliability, and rapid response to renewable energy sources. Currently, noble metals and their oxides—such as Pt, IrO₂, and RuO₂—remain the most widely used and high active electrocatalysts in acidic media to accelerate the water electrolysis processes. However, their large-scale pratical application is severely hindered by the factors such as scarcity and the trade-off between activity and stability. Recently, the integration of artificial intelligence (AI) with high-throughput synthesis technology has demonstrated an increasingly vital role in material screening, enabling the design of highly efficient and cost-effective catalysts. This paper first reviews the fundamental catalytic mechanisms of the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) in acidic media. Then, it summarizes the design strategies and prevailing challenges for noble metal catalysts in acidic water electrolysis. Finally, it presents several data-driven, synergistic approaches enabled by AI in noble metal catalyst research and development (R&D), along with the latest progress, current challenges, and future prospects.

More than 2 billion tons of coal-based solid wastes (CBSW) are produced annually in China present not only significant environmental hazards, including air pollution from dust, soil degradation, and water contamination from heavy metals, but also direct safety risks such as spontaneous combustion and landslides. Currently, soil degradation is becoming an increasingly serious concern. Artificial soil is a crucial green construction material. However, the current resource utilization of CBSW in artificial soil is confronted with difficulties such as low efficiency, high ecological risks, and obstacles to industrialization. Therefore, there is an urgent requirement to develop a stable and eco-friendly approach for the construction of artificial soil. This paper reviews the physicochemical properties of CBSW and its adaptability to soil improvement. Considering the application directions of CBSW in ecological soil (such as remediating contaminated soil, improving poor soil quality, and promoting plant growth). It focuses on key methods for preparing artificial soil. These methods include pretreatment technology, optimizing the ratio of solid waste, additives, and soil, and evaluating ecological effects. This work provides insights into transforming coal waste into a valuable resource for ecological restoration.

The urgent need for high-performance energy storage devices has been driving the quest for superior battery-type electrode materials for hybrid supercapacitors (HSCs), however the relevant synthesis methods are usually tedious and poorly affordable. In this paper, a two-step route was elaborated to prepare Ni, Co hydroxide/N-doped porous carbon (Ni_xCo_1-x(OH)₂/NPC) nanocomposites for hybrid supercapacitors. NPC with unique three-dimensional interconnected porous structure was obtained by HIPE high internal phase emulsion (HIPE) polymerization with subsequent pyrolysis. The NPC can act not only as a conductive network providing abundant accessible area and convenient charge transfer routes, but also as an anchoring platform for Ni_xCo_1-x(OH)₂ growth via chemical bath deposition (CBD) without agglomeration. By tuning Ni²⁺/Co²⁺ ratio, the optimized Ni_xCo_1-x(OH)₂/NPC nanocomposite exhibited excellent electrochemical performance with a capacity of 1392 F g^-1 at 1 A g^-1 in 6 M KOH solution. Furthermore, coupling with an activated NPC anode, the assembled hybrid supercapacitor possessed an appreciable energy density of 118.9 Wh kg^-1 at 400.0 W kg^-1 and a capacitance retention ratio of 80.7 % after 5000 charge-discharge cycles, showing considerable application prospects. This work provides new inspirations for the reasonable design and optimization of new electrode materials for first-rate hybrid supercapacitors.

This study investigates a novel phenomenon of non-Hermitian phonon quantization in Eu³⁺: BiPO₄ crystals controlled by different phases. The hexagonal (H) -phase (0.5:1) with low symmetry has strongest destructive gamma phonon quantization as compare to high symmetry phases H-Monoclinic (M) phases (12:1, 1:1) in fluorescence (FL) region, while strong constructive dominant dressing quantization exhibits due to higher phonon density of states in spontaneous four wave mixing (SFWM) region. Strong Spectral Autler-Townes (SAT) is observed in (6:1) phase at small angle and time gate position (GP = 500 ns), while, strong Temporal Autler-Townes (TAT) is studied at GP = 1ns. Also, (6:1) exhibits angle destructive quantization in FL region. Comparison between the H-M (12:1) phase and H = M (6:1) phases reveals that the H-M phase exhibits stronger destructive gamma quantization in FL region due to larger gamma phonon in (12:1) phase. Moreover, H-phase (0.5:1) exhibits large number of phonon density and shows strong constructive quantization as compare to M-phase (7:1) in SWFM region. Additionally, two destructive dressing quantization are observed in fluorescence region where gamma quantization is affected by additional laser. This work establishes a deterministic relation between non-Hermitian phonon quantization and different phases of Eu³⁺: BiPO₄, enabling applications in quantum memory and tunable bandpass filters. The Band pass filter control through phonon quantization with different phases of Eu³⁺: BiPO₄.

Fast-charging sodium-ion batteries are severely constrained by sluggish Na⁺ diffusion, structural instability, and rapid capacity fading in layered anodes, representing a major challenge for high-power energy storage applications. Here, a Co and Se co-doping strategy is implemented on MoS₂ (Co-MoS_1.8Se_0.2/C) to stabilize the metallic 1T-like phase, expand interlayer spacing, and introduce abundant defect sites, generating additional redox-active centers that facilitate rapid and reversible Na⁺ insertion and extraction. Cobalt doping serves as a catalytic regulator, promoting uniform SEI formation and enhancing interfacial stability, whereas selenium doping reduces Na⁺ diffusion barriers and alleviates strain induced by volumetric changes. A conductive carbon framework supports the nanosheet structure, prevents restacking, and buffers mechanical stress, ensuring structural integrity during extreme-rate cycling. The Co-MoS_1.8Se_0.2/C electrode achieves a reversible capacity of 250 mAh g^-1 at 20 A g^-1, corresponding to full charge/discharge in approximately 15 s, and maintains long-term cycling stability over 1400 cycles at 5 A g^-1. Structural analyses reveal partial electron transfer from Co and Se to Mo upon intercalation, triggering reorganization of Mo 4d orbitals and inducing a spontaneous 2H-to-1T phase transition, which enhances electrical conductivity. Reversible layered-to-metallic transformation occurs alongside the formation of a stable SEI layer, further promoting electrochemical kinetics and interfacial stability. The synergistic integration of dual-element doping and carbon framework design significantly improves structural robustness and sodium storage performance.

Photocatalytic nitrogen reduction to produce ammonia under ambient conditions is a promising green route. In this study, we demonstrate that one-dimensional (1D) TiO₂ nanobelts, prepared via a facile hydrothermal method, can convert gaseous dinitrogen to ammonia in pure water under light irradiation. The photocatalytic ammonia production performance of TiO₂ nanobelts can be further enhanced through acid corrosion treatment, which generates secondary nanostructures. These surface nanostructures serve as potential catalytically active sites for nitrogen adsorption and reduction. Both photoelectrochemical measurements and photoluminescence spectra confirm that the optimized TiO₂ nanobelts exhibit improved charge separation. Given their excellent stability and unique 1D nanostructure, acid-corroded TiO₂ nanobelts are a promising support material for constructing high-performance composite photocatalysts for nitrogen fixation.

Arsenic pollution in water poses a significant environmental challenge due to its high toxicity and non-degradability. In this study, FeMn-layered double hydroxide (FeMn-LDH) was synthesized using hydrothermal and coprecipitation methods with different precursors for electrochemical arsenic remediation. The crystallinity of FeMn-LDH was enhanced with nitrate precursor compared to chloride precursor. The corresponding calcined layered double oxide obtained through the coprecipitation method (FMO-NO₃-Co) exhibits a significantly increased specific surface area and an optimal average pore size, facilitating efficient ion transport, and enhanced oxidation state of Mn, increasing arsenic removal efficiency. Electrochemical tests indicate that FMH-NO₃-Co exhibits relatively high specific capacitance and excellent electrochemical performance. Notably, the FMO-NO₃-Co achieves an electrosorption capacity of 55.5 mg g^-1 with 56.6 % of As(Ⅲ) being electrochemically oxidized, demonstrating superior electrocatalytic activity for the oxidation of As(Ⅲ) and high-performance electrosorption of As(V). The arsenic removal mechanism was comprehensively analyzed, revealing that Mn²⁺/Mn³⁺ redox cycling played a key role in As(Ⅲ) oxidation, while Fe-based coordination sites contributed to As(V) adsorption. Furthermore, the enhanced porosity and conductivity of the calcined LDH materials significantly improved charge transfer efficiency, thereby accelerating the arsenic removal process. Overall, this study provides valuable insights into the potential application of FeMn-LDH in electrochemical arsenic remediation.

The development of efficient, stable, and low-cost electrocatalysts is crucial for hydrogen production via water electrolysis. While multi-principal element alloys (MPEAs) show great potential due to their multi-component synergy and tunable electronic structures, their practical application is often hampered by insufficient active sites and poor long-term stability. Herein, we report a phase-engineering-guided dealloying strategy to fabricate a high-performance MPEA catalyst for hydrogen evolution reaction (HER). This approach employs a triple-phase Al₆₀Ni₂₇Fe₅Co₅Mo₃ precursor, wherein chemical dealloying in an alkaline medium transforms the BCC parent phase into an ordered B2 phase, while completely dissolving the less stable FCC and tetragonal phases. This process results in a unique heterogeneous structure of Ni-based oxide nanocrystals enveloped by a Mo-rich metallic glass phase, coating the B2 phase surface. Benefiting from the abundant heterogeneous interfaces and synergistic interactions among multiple phases generated during dealloying, the catalyst exhibits outstanding activity and stability for HER in alkaline media, achieving a low overpotential of 35 mV at 10 mA cm^-2 and exceptional durability for 500 h at 100 mA cm^-2 with negligible activity degradation. This work presents a novel pathway for designing multiphase MPEAs and underscores the significant potential of high-performance electrocatalyst preparation by combining phase engineering with dealloying.