Food fraud in high-value seafood products, particularly mislabeling of geographic origin and production method, poses a growing challenge to market transparency and consumer protection. Mullet (Mugil cephalus) roe is a representative case, as products of different origins are difficult to visually distinguish, and conventional DNA-based approaches are insufficient to resolve intra-species variation. In this study, high-resolution mass spectrometry (HRMS)-based small-molecule fingerprinting was applied to discriminate mullet roe by production method (wild vs farmed) and geographic origin (Taiwan wild vs imported). Partial least squares discriminant analysis (PLS-DA) successfully discriminated imported, Taiwan wild, and Taiwan farmed mullet roe, while orthogonal PLS-DA (OPLS-DA) further resolved differences between the two wild sources (imported vs Taiwan wild). A total of 26 and 10 compounds were statistically selected, structurally annotated with Schymanski Level 2 confidence, and retained as markers of production method and geographic origin, respectively. The identified markers included lipid derivatives, vitamin A derivatives, algal-associated metabolites, and anthropogenic compounds, reflecting differences in diet, habitat, and environmental exposure. These findings demonstrate that small-molecule fingerprinting captures origin-specific biochemical signatures and provides a scientific basis for developing rapid screening or targeted analytical methods for origin authentication in regulatory, food control, and industrial settings, contributing to the prevention of food fraud and the protection of consumer rights.
Background Transformation of biomass waste into catalyst supports for upgrading bio-based chemicals has attracted significant attention. This approach not only mitigates the detrimental environmental impacts of agricultural wastes but also produces valuable functionalized materials for catalytic conversion of biomass to fine chemicals. Method The activated biochar-anchored Ni nanoparticles (Ni@ABC) catalyst was synthesized via a two steps process. First, activated biochar (ABC) was prepared through pyrolysis of a mixture of water hyacinth and H3PO4. Then, Ni nanoparticles (Ni NPs) were embedded into the synthesized ABC structure using an impregnation method. The physical and chemical characteristics of Ni@ABC catalyst were measured using various techniques. Significant findings The characterization results reveals that the Ni@ABC catalyst exhibited a mesoporous structure, high Brunauer-Emmett-Teller (BET) specific surface area, uniformly dispersed and small Ni nanoparticles, and abundant oxygen- and phosphorous-containing functional groups. The 10Ni@ABC catalyst efficiently converted furfural (FA) to tetrahydrofurfural alcohol (THFA), achieving a high yield of 98.8% at 50 °C for 5 h using NaBH4 as a hydrogen source, outperforming the convention metal oxides-supported Ni catalysts. The enhanced catalytic activity is attributed to the synergistic effects between Ni NPs and functional groups within the ABC support. Moreover, the reaction kinetics and mechanism were systematically investigated.
Atmospheric CO2 emissions are driving the climate crisis, prompting research into catalytic hydrogenation to produce value-added chemicals. Here, a ternary CoCuAl mixed-metal oxide catalyst derived from collapsed layered double hydroxides (LDHs) was developed for selective CO2 hydrogenation to C5+ hydrocarbons at 240 degrees C. Compared with LDH-derived CuAl (mainly yielded CO and methanol) and CoAl (mainly methane) catalysts, the ternary CoCuAl catalyst with a Co/(Co + Cu) ratio of 0.53 exhibited a remarkable shift in selectivity toward C5+ hydrocarbons (12.2%) due to synergy between Cu and Co. Cu promoted CO2-to-CO conversion via the reverse water-gas shift (RWGS) reaction, while Co enabled subsequent chain growth through Fischer-Tropsch synthesis (FTS). The low temperature of 240 degrees C was essential, as higher temperatures likely cause Cu-Co segregation and metallic Co particles that favor methanation over FTS. Importantly, the LDH-derived catalyst outperformed the conventional co-precipitated CoCuAl analog, delivering higher CO2 conversion (16.3% vs. 9.5%) and C5+ selectivity (12.2% vs. 5.0%), owing to superior metal dispersion from the LDH confinement effect. XPS confirmed strong Co-Cu interactions, and in-situ IR spectroscopy revealed key CO and formate intermediates. This work highlights the potential of LDH-derived mixed oxides for efficient CO2 valorization under mild conditions.
Accurate direction-of-arrival (DOA) estimation for sound sources is challenging due to the continuous changes in acoustic characteristics across time and frequency. In such scenarios, accurate localization relies on the ability to aggregate relevant features and model temporal dependencies effectively. In time series modeling, achieving a balance between model performance and computational efficiency remains a significant challenge. To address this, we propose FAS-Conformer, an efficient feature aggregation enhanced Swift-Conformer network. The proposed framework first employs a feature aggregation module to enhance informative time-frequency features along the temporal and spectral dimensions. The aggregated representations are then fed into a Swift-Conformer for sequential modeling. The Swift-Conformer is a lightweight Conformer architecture that combines state-space modeling with self-attention mechanisms, enabling the model to capture long-term temporal dependencies and contextual information at high inference speeds. To further reduce complexity, feedforward compression and a temporal shift operation are introduced into the convolutional layers, which expand the temporal receptive field without increasing the kernel size. This design enables effective temporal representation by combining long-term temporal structure with selective long-range dependency information, without introducing excessive computational cost. Extensive experiments further demonstrate that FAS-Conformer exhibits superior localization performance and inference speed for dynamic sound sources under low signal-to-noise ratio conditions across diverse noise environments.
A viscoelastic model of a material with brick-and-mortar architecture is proposed in this paper. This model can be used to simulate the uniaxial mechanical behavior of natural bone tissue and man-made bone-like materials. Using the unit cell approach and the formulation in the time domain, we derive the closed-form responses of bone and bone-like materials under different loading paths. These closed-form solutions enable us to investigate the influence of microstructures on the effective properties of the bone and bone-like materials, including the instantaneous modulus and viscosity; the asymptotic modulus and viscosity; the relaxation; the creep; the toughness; and the energy dissipation. Our study shows the different effects of the volume fraction and the aspect ratio of the inclusion. It also exhibits the effect of the staggering patterns of matrix and inclusion on the mechanical features of the bone-like material under the stress- and strain-controlled cases. This research supports the design of bone-like materials with specified performance theoretically and it also provides a foundation for future computational design of bone-like materials.
