The detection of multiple trace analytes using single sensors is often impeded by the limited sensitivity of material and the interference form overlapping signals in complex mixtures. Here, we introduce an efficient and durable heterostructured high-entropy alloy (HEA) material, where non-noble HEA nanoparticles are used to disperse and stabilize Pt clusters (denoted as HEA@Pt). The HEA@Pt exhibits high sensitivity to three trace analytes (dopamine, uric acid, and paracetamol) during the electrochemical detection process, leveraging its multifunctional catalytic sensing capabilities for diverse mixtures. Additionally, to address the challenge of signal overlap, we integrate a recurrent neural network into multimodal sensing, combined with machine learning (ML) algorithms to accurately identify multiple analytes in mixtures. After five-fold cross-validation, the prediction accuracy deviations for dopamine, uric acid, and paracetamol were 3.20, 9.18 and 3.84, respectively, with goodness-of-fit values of 0.984, 0.992 and 0.990. The model achieved a prediction accuracy of 96.67% for unknown mixture samples, demonstrating robust generalization performance. This approach of multifunctional HEA combined with ML algorithms effectively overcomes detection errors caused by the complex detection of multiple chemical substances and the overlap of multiple response signals, thereby enabling accurate and reliable identification of multi-target analytes.

Lithium iron phosphate (LFP) offers excellent structural and performance stability derived from the (PO₄)³⁻ polyanionic structure, which is beneficial for long-term usage. However, this inherent stability also comes along with intrinsically poor ionic and electronic conductivities, which have been notoriously plaguing its high-rate performance and broader applications. Here, we present a gas-assisted transient synthesis (GATS, ~ 30 s) of LFP with controllable oxygen vacancies (O_v) for enhanced rate performance yet without sacrificing structural integrity or cycling stability. Benefited by the ultrafast heating and a higher synthesis temperature, we revealed that the LFP synthesis in GATS followed an interface reaction mechanism (rapid core shrinking) with a low activation energy (E_a), thus reducing the synthesis time from ~ 16.5 h in tube furnace heating (TFH, often nuclei-growth mechanism) to merely seconds. The optimized LFP sample demonstrates an 8-fold enhancement in ionic conductivity and a 12-fold increase in electronic conductivity compared to LFP obtained by TFH and attains exceptional cycling stability even at high rates of 10 C, as evidenced by a higher capacity retention of 93.8% (vs. 63.6% of commercial LFP) after 1000 cycles. Our strategy offers a kinetic pathway for rapid synthesis and structural engineering of LFP, thus unlocking its potential for broader energy storage applications.

Metal 3D printing holds great promise for future digitalized manufacturing. However, the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization. Meanwhile, the “optimized” yet fixed parameters largely limit possible extensions to new designs and materials. Herein, we report a high throughput design coupled with machine learning (ML) guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance. The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties, while ML is used for data analysis by model building for prediction (optimization), and understanding. For 316L stainless steel, we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis. An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57% to below 0.1%. Accordingly, the ML-recommended samples showed higher tensile strength (609.28 MPa) and elongation (50.67%), superior anti-corrosion (I_corr = 4.17 × 10⁻⁸ A·cm⁻²), and stable alkaline oxygen evolution for >100 hours (at 500 mA·cm⁻²). Remarkably, through the correlation analysis of printing parameters and targeted properties, we find that the influence of hardness on corrosion resistance is second only to porosity. We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density, thus demonstrating the method’s general extensibility and efficiency. Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.

The chemoselective hydrodeoxygenation of natural lignocellulosic materials plays a crucial role in converting biomass into value-added chemicals. Yet their complex molecular structures often require multiple active sites synergy for effective activation and achieving high chemoselectivity. Herein, it is reported that a high-entropy alloy (HEA) on high-entropy oxide (HEO) hetero-structured catalyst for highly active, chemoselective, and robust vanillin hydrodeoxygenation. The heterogenous HEA/HEO catalysts were prepared by thermal reduction of senary HEOs (NiZnCuFeAlZrO_x), where exsolvable metals (e.g., Ni, Zn, Cu) in situ emerged and formed randomly dispersed HEA nanoparticles anchoring on the HEO matrix. This catalyst exhibits excellent catalytic performance: 100% conversion of vanillin and 95% selectivity toward high-value 2-methyl-4 methoxy phenol at low temperature of 120 ℃, which were attributed to the synergistic effect among HEO matrix (with abundant oxygen vacancies), anchored HEA nanoparticles (having excellent hydrogenolysis capability), and their intimate hetero-interfaces (showing strong electron transferring effect). Therefore, our work reported the successful construction of HEA/HEO heterogeneous catalysts and their superior multifunctionality in biomass conversion, which could shed light on catalyst design for many important reactions that are complex and require multifunctional active sites.

Heterogeneous nanostructured metals are emerging strategies for achieving both high strength and ductility, which are particularly attractive for high entropy alloys (HEAs) to combine the synergistic enhancements from multielement composition, grain boundaries, and heterogeneity effects. However, the construction of heterogeneous nanostructured HEAs remains elusive and can involve delicate processes that are not practically scalable. Herein we report using composition design (i.e., enthalpy engineering) to create hierarchical, nanostructured HEAs as demonstrated by adding Ni into FeCrCoAlTi_0.5 HEA. The strong enthalpic interaction between (Ni,Co) and (Al,Ti) pairs in FeCrCoAlTi_0.5Ni_x (x = 0.5–1.5) induced phase partitions into B2 (ordered phase, hard) matrix and A2 (disordered phase, soft) precipitates, resulting in a hierarchical structure of B2 grains and sub-grains of near-coherent A2 nanodomains (~ 12.5 nm) divided by A2 interdendritic regions. As a result, the FeCrCoAlTi_0.5Ni_1.5 HEA with this unique hierarchical nanostructure exhibits the best combination of strength and plasticity, i.e., a 2-fold increase in compressive strength (2.60 GPa) and significant enhancement of plastic strain (15.8%) as compared with the original FeCrCoAlTi_0.5 HEA. Enthalpy analysis and simulation study reveal the phase partition process during cooling induced by an enthalpy-driven order-disorder transition while the order parameters illustrate the strong ordering in (Ni,Co)(Al,Ti)-rich B2 phase and high entropy mixing in less interactive FeCrCo-rich A2 phase. Our work therefore provides a strategy for hierarchical nanostructured HEA formation by composition design considering enthalpy and entropy interplay.