Photothermal Structural Dynamics of Au Nanofurnace for In Situ Enhancement in Desorption and Ionization

Machine Learning-Assisted Prediction of Photothermal Metal-Phenolic Networks

Photothermal therapy (PTT) demonstrates significant potential in cancer treatment, wound healing, and antibacterial therapy, with its efficacy largely depending on the performance of photothermal agents (PTAs). Metal-phenolic network (MPN) materials are ideal PTA candidates due to their low cost, good biocompatibility and excellent ligand-to-metal charge transfer properties. However, not all MPNs exhibit significant photothermal properties, and the vast chemical space of MPNs (over 700,000 potential combinations) complicates the screening of high-photothermal materials. To address this challenge, this study introduces machine learning (ML) methods for predicting the photothermal performance of MPNs. A database of photothermal properties of 80 modular MPNs was constructed, and the ML process was optimized through feature engineering and model training. The selected extreme gradient boosting model (XGBoost) successfully identified 1,654 high photothermal MPNs from a virtual database of 44,438. Subsequent experimental validation revealed a remarkable success rate of 70 % in predicting high photothermal MPNs. Additionally, several previously unreported high photothermal MPNs were discovered, demonstrating advantages in photothermal antibacterial applications. This study offers an innovative ML-driven approach for the efficient screening of MPN materials, providing a solid foundation for PTA design in PTT and other biomedical applications.

Noble Metal Nanoparticle Assisted Mass Spectrometry for Metabolite-Based In Vitro Diagnostics

In vitro diagnostics (IVD) makes clinical diagnosis rapid, simple, and noninvasive to patients, playing a crucial role in the early diagnosis and monitoring of diseases. Metabolic biomarkers are closely correlated to the phenotype of diseases. However, most IVD platforms are constrained by the sensitivity and throughput of assay. In recent years, noble-metal-nanoparticle (NMNP)-assisted laser desorption/ionization mass spectrometry (LDI MS) has generated major advances in metabolite analysis, significantly improving the sensitivity, accuracy, and throughput of IVD due to the unique optical and electrical properties of NMNPs. This review systematically assesses the development of NMNPs as LDI MS matrices in the detection of metabolites for IVD application. The analysis of several NMNP structures, such as core-shell, porous, and 2D nanoparticles, elucidates their significant contribution to the enhancement of MS performance. Furthermore, the recent advancements in the application of NMNPs for diagnosing various systemic diseases are summarized. Finally, the prospects and challenges of NMNP-assisted MS for IVD are discussed. This review elucidates the roles of NMNPs' structure in enhancing MS-based metabolic detection and provides an overview of various IVD applications, consequently offering comprehensive insights for researchers and developers in this field.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Supra-Photothermal CO2 Methanation over Greenhouse-Like Plasmonic Superstructures of Ultrasmall Cobalt Nanoparticles

Improving the solar-to-thermal energy conversion efficiency of photothermal nanomaterials at no expense of other physicochemical properties, e.g., the catalytic reactivity of metal nanoparticles, is highly desired for diverse applications but remains a big challenge. Herein, a synergistic strategy is developed for enhanced photothermal conversion by a greenhouse-like plasmonic superstructure of 4 nm cobalt nanoparticles while maintaining their intrinsic catalytic reactivity. The silica shell plays a key role in retaining the plasmonic superstructures for efficient use of the full solar spectrum, and reducing the heat loss of cobalt nanoparticles via the nano-greenhouse effect. The optimized plasmonic superstructure catalyst exhibits supra-photothermal CO2 methanation performance with a record-high rate of 2.3 mol gCo -1 h-1 , close to 100% CH4 selectivity, and desirable catalytic stability. This work reveals the great potential of nanoscale greenhouse effect in enhancing photothermal conversions through the combination with conventional promoting strategies, shedding light on the design of efficient photothermal nanomaterials for demanding applications.

Wearable fabric-based ZnO nanogenerator for biomechanical and biothermal monitoring

Wearable devices that can mechanically conform to human skin are a necessity for reliable monitoring and decoding of biomechanical activities through skin. Most inorganic piezoelectrics, however, lack deformability and damage tolerance, impeding stable motion monitoring. Here, we present an air-permeable fabric-based ZnO nanogenerator with mechanical adaptivity to diverse deformations for wearable piezoelectric sensors, collecting biomechanical health data. We fabricate ZnO nanorods incorporated throughout the entire nylon fabric, with a strategically positioned neutral mechanical plane, for bending-sensitive electronics (2.59 μA mm). Its hierarchically interlocked geometry also permits sensitive tactile sensing (0.15 nA kPa-1). Various physiological information about activities, including pulse beating, breathing, saliva swallowing, and coughing, is attained using skin-mounted sensors. Further, the pyroelectric sensing capability of a mask-attached device is demonstrated by identifying specific respiratory patterns. Our wearable healthcare sensors hold great promise for real-time monitoring of health-related vital signs, informing individuals' health status without disrupting their daily lives.

A Surface Matrix of Au NPs Decorated Graphdiyne for Multifunctional Laser Desorption/Ionization Mass Spectrometry

The development of the valid strategy to enhance laser desorption/ionization efficiency gives rise to widespread concern in surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) technology. Herein, a hybrid of Au NP-decorated graphdiyne (Au/GDY) was fabricated and employed as the SALDI-MS matrix for the first time, and a mechanism based on photothermal and photochemical energy conversions was proposed to understand LDI processes. Given theoretical simulations and microstructure characterizations, it was revealed that the formation of a coupled thermal field and internal electric field endow the as-prepared Au/GDY matrix with superior desorption and ionization efficiency, respectively. Moreover, laser-induced matrix ablation introduced strain and defect level into the Au/GDY hybrid, suppressing the recombination of charge carriers and thereby facilitating analyte ionization. The optimized Au/GDY matrix allowed for reliable detection of trace sulfacetamide and visualization of exogenous/endogenous components in biological tissues. This work offers an integrated solution to promote LDI efficiency based on collaborative photothermal conversion and internal electric field, and may inspire the design of novel semiconductor-based surface matrices.

Phage-targeting bimetallic nanoplasmonic biochip functionalized with bacterial outer membranes as a biorecognition element

The use of phages-a natural predator of bacteria-has emerged as a therapeutic strategy for treating multidrug-resistant bacterial infections; thus, the isolation and detection of phages from the environment is crucial for advancing phage therapy. Herein, for the first time, we propose a nanoplasmonic-based biodetection platform for phages that utilizes bacterial outer membranes (OMs) as a biorecognition element. Conventional biosensors based on phage-bacteria interactions encounter multiple challenges due to the bacteriolytic phages and potentially toxic bacteria, resulting in instability and risk in the measurement. Therefore, instead of whole living bacteria, we employ a safe biochemical OMs fraction presenting phage-specific receptors, allowing the robust and reliable phage detection. In addition, the biochip is constructed on bimetallic nanoplasmonic islands through solid-state dewetting for synergy between Au and Ag, whereby sensitive detection of phage-OMs interactions is achieved by monitoring the absorption peak shift. For high detection performance, the nanoplasmonic chip is optimized by systematically investigating the morphological features, e.g., size and packing density of the nanoislands. Using our optimized device, phages are detected with high sensitivity (≥∼104 plaques), specificity (little cross-reactivity), and affinity (stronger binding to the host OMs than anti-bacterial antibodies), further exhibiting the cell-killing activities.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

Breathing-Driven Self-Powered Pyroelectric ZnO Integrated Face Mask for Bioprotection

Rapid spread of infectious diseases is a global threat and has an adverse impact on human health, livelihood, and economic stability, as manifested in the ongoing coronavirus disease 2019 (COVID-19) pandemic. Even though people wear a face mask as protective equipment, direct disinfection of the pathogens is barely feasible, which thereby urges the development of biocidal agents. Meanwhile, repetitive respiration generates temperature variation wherein the heat is regrettably wasted. Herein, a biocidal ZnO nanorod-modified paper (ZNR-paper) composite that is 1) integrated on a face mask, 2) harvests waste breathing-driven thermal energy, 3) facilitates the pyrocatalytic production of reactive oxygen species (ROS), and ultimately 4) exhibits antibacterial and antiviral performance is proposed. Furthermore, in situ generated compressive/tensile strain of the composite by being attached to a curved mask is investigated for high pyroelectricity. The anisotropic ZNR distortion in the bent composite is verified with changes in ZnO bond lengths and OZnO bond angles in a ZnO₄ tetrahedron, resulting in an increased polarization state and possibly contributing to the following pyroelectricity. The enhanced pyroelectric behavior is demonstrated by efficient ROS production and notable bioprotection. This study exploring the pre-strain effect on the pyroelectricity of ZNR-paper might provide new insights into the piezo-/pyroelectric material-based applications.

Laser-Shock-Driven In Situ Evolution of Atomic Defect and Piezoelectricity in Graphitic Carbon Nitride for the Ionization in Mass Spectrometry

Nanostructures─coupled with mass spectrometry─have been intensively investigated to improve the detection sensitivity and reproducibility of small biomolecules in laser desorption/ionization mass spectrometry (LDI-MS). However, the impact of laser-induced shock wave on the ionization of the nanostructures has rarely been reported. Herein, we systematically elucidate the laser shock wave effect on the ionization in terms of the in situ development of atomic defects and piezoelectricity in two-dimensional graphitic carbon nitride nanosheets (g-C₃N₄ NS) by short laser pulses. The mass analysis results of immunosuppressive drugs verify the enhanced LDI-MS performance, structurally originating from anisotropic lattice distortions in g-C₃N₄ NS, i.e., in-plane extension (contraction) and out-of-plane contraction (extension) that modulate the charge carrier motion. Along with the experimental investigations, density functional theory calculations on Mulliken charges and dipole moments demonstrate the contribution of defect and piezoelectricity to the ionization. The results of this study provide a mechanistic understanding of the underlying ionization processes, which is crucial for revealing the full potential of laser shock waves in LDI-MS.

A Copper-Based Biosensor for Dual-Mode Glucose Detection

Glucose is a source of energy for daily activities of the human body and is regarded as a clinical biomarker, due to the abnormal glucose level in the blood leading to many endocrine metabolic diseases. Thus, it is indispensable to develop simple, accurate, and sensitive methods for glucose detection. However, the current methods mainly depend on natural enzymes, which are unstable, hard to prepare, and expensive, limiting the extensive applications in clinics. Herein, we propose a dual-mode Cu₂O nanoparticles (NPs) based biosensor for glucose analysis based on colorimetric assay and laser desorption/ionization mass spectrometry (LDI MS). Cu₂O NPs exhibited excellent peroxidase-like activity and served as a matrix for LDI MS analysis, achieving visual and accurate quantitative analysis of glucose in serum. Our proposed method possesses promising application values in clinical disease diagnostics and monitoring.

Laser desorption/ionization mass spectrometry of L-thyroxine (T4) using combi-matrix of α-cyano-4-hydroxycinnamic acid (CHCA) and graphene

An optimal combi-matrix for MALDI-TOF mass spectrometry was presented for the analysis of L-thyroxine (T4) in human serum. For the selection of the optimal combi-matrix, several kinds of combi-matrices were prepared by mixing the conventional organic matrix of CHCA with nanomaterials, such as graphene, carbon nanotubes, nanoparticles of Pt and TiO2. In order to select the optimal combi-matrix, the absorption at the wavelength of laser radiation (337 nm) for the ionization of sample was estimated using UV–Vis spectrometry. And, the heat absorption properties of these combi-matrices were also analyzed using differential scanning calorimetry (DSC), such as onset temperature and fusion enthalpy. In the case of the combi-matrix of CHCA and graphene, the onset temperature and fusion enthalpy were observed to be lower than those of CHCA, which represented the enhanced transfer of heat to the analyte in comparison with CHCA. From the analysis of optical and thermal properties, the combi-matrix of CHCA and graphene was selected to be an optimal combination for the transfer of laser energy during MALDI-TOF mass spectrometry. The feasibility of the combi-matrix composed of CHCA and graphene was demonstrated for the analysis of T4 molecules using MALDI-TOF mass spectrometry. The combi-matrix of CHCA and graphene was estimated to have an improved limit of detection and a wider detection range in comparison with other kinds of combi-matrices. Finally, the MALDI-TOF MS results of T4 analysis using combi-matrix were statistically compared with those of the conventional immunoassay.