Carboxyl-functionalized two-dimensional MXene-Au nanocomposites were prepared as SERS substrates for the detection of melamine in dairy products.

In the present study, we address the limitations of conventional surface-enhanced Raman scattering (SERS) techniques for sensitive and stable detection of melamine in food products, especially dairy. To overcome these challenges, we developed a novel SERS-active substrate by incorporating gold nanoparticles (AuNPs) onto carboxyl-functionalized two-dimensional (2D) MXene material doped with nitrides, specifically Au-Ti2N-COOH. Our strategy leverages the unique physicochemical properties of MXene, a class of atomically thin, 2D transition metal carbides/nitrides, with tunable surface functionalities. By modifying the MXene surface with AuNPs and introducing carboxyl groups (-COOH), we successfully enhanced the interaction between the substrate and melamine molecules. The carboxyl groups form hydrogen bonds with the amino groups on the melamine's triazine ring, facilitating the adsorption of melamine molecules within the 'hotspot' regions responsible for SERS signal amplification. A series of characterization methods were used to confirm the successful synthesis of Au-Ti2N-COOH composites.Using Au-Ti2N-COOH as the SERS substrate, we detected melamine in spiked dairy product samples with significantly enhanced sensitivity and stability compared to nitride-doped MXene alone. The detection limit in liquid milk stands at 3.7008 µg kg-1, with spike recovery rates ranging from 99.84% to 107.55% and an approximate RSD of 5%. This work demonstrates the effectiveness of our approach in designing a label-free, rapid, and robust SERS platform for the accurate quantitation of melamine contamination in food, thereby mitigating health risks associated with melamine adulteration.

Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification.

Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.