Anchoring Frustrated Lewis Pair Active Sites on Copper Nanoclusters for Regioselective Hydrogenation.

In recent years, the concept of Frustrated Lewis Pairs (FLPs), which consist of a combination of Lewis acid (LA) and Lewis base (LB) active sites arranged in a suitable geometric configuration, has been widely utilized in homogeneous catalytic reactions. This concept has also been extended to solid supports such as zeolites, metal oxide surfaces, and metal/covalent organic frameworks, resulting in a diverse range of heterogeneous FLP catalysts that have demonstrated notable efficiency and recyclability in activating small molecules. This study presents the successful immobilization of FLP active sites onto the surface of ligand-stabilized copper nanoclusters with atomic precision, leading to the development of copper nanocluster FLP catalysts characterized by high reactivity, stability, and selectivity. Specifically, thiol ligands containing 2-methoxyl groups were strategically designed to stabilize the surface of [Cu34S7(RS)18(PPh3)4]2+ (where RSH = 2-methoxybenzenethiol), facilitating the formation of FLPs between the surface copper atoms (LA) and ligand oxygen atoms (LB). Experimental and theoretical investigations have demonstrated that these FLPs on the cluster surface can efficiently activate H2 through a heterolytic pathway, resulting in superior catalytic performance in the hydrogenation of alkenes under mild conditions. Notably, the intricate yet precise surface coordination structures of the cluster, reminiscent of enzyme catalysts, enable the hydrogenation process to proceed with nearly 100% selectivity. This research offers valuable insights into the design of FLP catalysts with enhanced activity and selectivity by leveraging surface/interface coordination chemistry of ligand-stabilized atomically precise metal nanoclusters.

Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis.

Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, organic biomacromolecules, and various ionic mixtures. These components offer valuable insights and can reflect an individual's physiological conditions. Here, we introduced an explainable deep learning (DL)-assisted wearable self-calibrating colorimetric biosensing analysis platform to efficiently and precisely detect the biomarker's concentration in sweat. Specifically, we have integrated the advantages of the colorimetric sensing method, adsorbing-swelling hydrogel, and explainable DL algorithms to develop an enzyme/indicator-immobilized colorimetric patch, which has reliable colorimetric sensing ability and excellent adsorbing-swelling function. A total of 5625 colorimetric images were collected as the analysis data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Zn2+, glucose, and Ca2+ in human sweats could be facilely classified and quantified with 100% accuracy via the convolutional neural network (CNN) model, and the testing results of actual sweats via the DL-assisted colorimetric approach are 91.7-97.2% matching with the classical UV-vis spectrum. Class activation mapping (CAM) was utilized to visualize the inner working mechanism of CNN operation, which contributes to verify and explicate the design rationality of the noninvasive biosensing technology. An "end-to-end" model was established to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for health monitoring, disease prevention, and clinical diagnosis.

Fluorescent Selectivity-Enhanced FRET Based on 3D Photonic Crystals for Multianalyte Sensing.

Fluorescence resonance energy transfer (FRET) finds widespread utility in biochemical sensing, single-molecule experiments, cell physiology, and various other domains due to its inherent simplicity and high sensitivity. Nevertheless, the efficiency of energy transfer between the FRET donor and acceptor is significantly contingent on the local photonic environment, a factor that limits its application in complex systems or multianalyte detections. Here, a fluorescent selectivity-enhanced acridine orange (AO)-aflatoxins (AFs) FRET system based on a range of 3D topological photonic crystals (PCs) was developed with the aim of enhancing the selectivity and discrimination capabilities of FRET. By exploring the angle-dependent characteristics of the photonic stopband, the stopband distribution across different 3D topological PCs pixels was investigated. This approach led to selective fluorescence enhancement in PCs that matched the stopbands, enabling the successful discrimination of six distinct aflatoxins and facilitating complex multianalysis of moldy food samples. In particular, the stopband, which was strategically positioned within the blue-purple structural color range, exhibited a strong alignment with the fluorescence peaks of both the FRET donor and acceptor. This alignment allowed the 3D three-pointed star PCs to be effectively employed for the identification of mixed samples containing six distinct aflatoxins as well as the detection of real aflatoxin samples present in moldy potatoes, bread, oats, and peanuts. Impressively, this approach achieved a remarkable accuracy rate of 100%. This innovative strategy not only presents a novel avenue for developing a multitarget discrimination analysis system but also offers a convenient pretreatment method for the quantitative detection of various aflatoxins.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis.

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Integrated Electrochemical Aptasensor Array toward Monitoring Anticancer Drugs in Sweat.

In the realm of clinical practice, the concurrent utilization of anticancer medications can enhance their overall therapeutic efficacy. However, it is crucial to acknowledge that the interactions among these anticancer drugs can potentially yield detrimental consequences on their intended outcomes. Consequently, the assessment of both anticancer potency and potential toxic side effects is greatly refined when multiple anticancer drugs are simultaneously detected and evaluated. Here, we designed a wearable electrochemical aptasensor array for monitoring multiple anticancer drugs in sweat. The integrated sensor array consists of three working electrodes modified with three different aptamers (Apt1, Apt2, and Apt3), a Au counter electrode, and a Ag/AgCl reference electrode. Molecular docking simulations were performed to show the binding affinities between three anticancer drugs and their corresponding aptamers. Various eigenvalues were derived from the square-wave voltammetry electrochemical signals, and these data sets were subjected to rigorous analysis through multivariate data analysis techniques. This analytical approach demonstrated exceptional performance by achieving flawless 100% accuracy in the precise identification of nine anticancer drugs consistently at uniform concentrations. Furthermore, the integrated wearable sensor array exhibited impressive capabilities, correctly recognizing all nine anticancer drugs with 100% accuracy and successfully distinguishing between these drugs in artificial sweat samples. The proposed sensor array presents good stability for 15 days. Flexibility tests showed stable device performance after 500 twisting cycles. This innovative wearable sensing array represents a novel approach for achieving real-time monitoring and precise adjustment of drug dosages. It offers invaluable insights for tailoring the treatment of anticancer drugs to individual patients, predicting both drug efficacy and potential adverse reactions within the field of clinical medicine.

Stable O3 Decomposition by Layered Double Hydroxides: The Pivotal Role of NiOOH Transformation.

Because ozone (O3) is a significant air pollutant, advanced O3 elimination technologies, particularly those under high-humidity conditions, have become an essential research focus. In this study, a nickel-iron layered double hydroxide (NiFe-LDH) was modified via intercalation with octanoate to develop an effective hydrophobic catalyst (NiFe-OAa-LDH) for O3 decomposition. The NiFe-OAa-LDH catalyst sustained its O3 decomposition rate of >98% for 48 h under conditions of 90% relative humidity, 840 L/(g·h) space velocity, and 100 ppm inlet O3 concentration. Moreover, it maintained a decomposition rate of 90% even when tested at a higher airflow rate of 2500 L/(g·h). Based on the changes induced by the Ni-OII to Ni-OIII bonds in NiFe-OAa-LDH during O3 treatment, catalytic O3 decomposition was proposed to occur in two stages. The first stage involved the reaction between the hydroxyl groups and O3, leading to the breakage of the O-H bonds, formation of NiOOH, and structural changes in the catalyst. This transformation resulted in the formation of abundant and stable hydrogen vacancies. According to density functional theory calculations, O3 can be effectively decomposed at the hydrogen vacancies with a low energy barrier during the second stage. This study provides new insights into O3 decomposition.

Study on the adsorption of phosphate by composite biochar of phosphogypsum and rape straw.

Wastewater containing phosphorus is often added by industrial activities, which is bad for the environment. In this study, composite biochar (PG-RS700) was prepared from phosphogypsum (PG) and rape straw (RS) for the treatment of phosphate in wastewater. SEM, FTIR, XRD and XPS characterization results showed that PG and RS were successfully combined. When PG-RS700 was dosed at 1.5 g/L and the phosphate solution concentration was 50 mg/L and pH = 8, the phosphate removal rate was 100% and the adsorption capacity was three times higher than the corresponding pure PG and RS. The quasi-secondary kinetic model indicated that the adsorption mechanism was chemisorption, and the maximum adsorption capacity for phosphate in the Langmuir isotherm model was 102.25 mg/g. Through pot experiment, the phosphorus adsorbed material obviously promoted the growth of plants. PG-RS700 can be used as a powerful adsorbent to treat phosphate in water and return it to soil as phosphate fertilizer.

Cactus-Inspired Photonic Crystal Chip for Attomolar Fluorescence Multi-analysis.

Automation and efficiency requirements of environmental monitoring are the pursuit of spontaneous sampling and ultrasensitivity for current sensory systems or detection apparatuses. In this work, inspired by cactus hierarchical structures, we develop a cactus-inspired photonic crystal chip to integrate spontaneous droplet sampling and fluorescence enhancement for sensitive multi-analyte detection. A conical hydrophilic pattern on hydrophobic surfaces can give rise to unidirectional Laplace pressure, which drives droplet transport to the assigned photonic crystal site. The nanostructure of photonic crystals has bigger capillarity to drive the droplet wetting uniformly into the photonic crystal matrix while performing prominent fluorescence enhancement by their photonic bandgap. A low to attomolar (2.24 × 10-19 M) fluorescence limit of detection (LOD) sensitivity can be achieved by the synergy of spontaneous droplet sampling and fluorescence enhancement. Focused on eutrophic water problems and algae pollution monitoring, a femtomolar (1.83 × 10-15 M) LOD and identification of various microcystins in urban environmental water can be achieved. The suitable integration of the unidirectional droplet transport by Laplace pressure and fluorescence enhancement by photonic crystals can achieve the spontaneous sampling and signal enhancement for ultratrace detections and sample survey of environmental monitoring and disease diagnosis.

Explainable Deep Learning-Assisted Photochromic Sensor for ß-Lactam Antibiotic Identification.

Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, "black-boxes". In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for ß-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six ß-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery.

Multi-Analyte Discriminated ECL via Photonic Crystal-Enhanced Selectivity.

Electrochemiluminescence (ECL) has numerous merits such as high sensitivity and specificity for the detection applications on pharmacy, food safety, immunoassay, disease diagnosis, environmental monitoring, nucleic acid assay, and clinical treatment. However, the insufficiency of ECL luminescent reagents is restricting their adoption on complex systems or multi-analyte detections. In this work, to improve the selectivity and discrimination of ECL detection with one or less luminescent reagent, we employed multi-stopband photonic crystals (PCs) to enhance assigned ECL. The discrimination of ECL was well investigated to establish the quantitative description with PC stopbands. The multi-stopband PC electrode can facilely achieve 10 antibiotics qualitative and quantitative analysis with 100% accuracy and 0.44 µM LOD in PBS buffer and human serum. The selectivity of ECL detection for multi-analytes can be improved via designed PC luminescence amplifications. The exploration on PC selectivity for ECL enhancement will promote the realistic application of the ECL technique and contribute to the facile and efficient optical platform for clinical or health monitoring.

Optical bistability modulation based on graphene sandwich structure with topological interface modes.

In this paper, we have investigated optical bistability modulation of transmitted beam that can be achieved by graphene sandwich structure with topological interface modes at terahertz frequency. Graphene with strong nonlinear optical effect was combined with sandwich photonic crystal to form a new sandwich structure with topological interface modes. The light-limiting properties of the topological interface modes, as well as its high unidirectionality and high transmission efficiency, all contribute positively to the reduction of the optical bistability threshold. In addition, the topological interface modes can effectively ensure the stability of the two steady state switching in the case of external interference. Moreover, optical bistability is closely related to the incident angle, the Fermi energy, the relaxation time, and the number of layers of graphene. Through parameter optimization, optical bistability with threshold of 105 V/m can be obtained, which has reached or is close to the range of the weak field.

Development of multiscale ultra-coarse-grained models for the SARS-CoV-2 virion from cryo-electron microscopy data.

The global spread of the new coronavirus COVID-19 has seriously affected human health and has caused a large number of deaths. Using molecular dynamics (MD) simulations to study the microscopic dynamic behavior of the virion provides an important means to study the pathogenic mechanism. In this work, we develop an ultra-coarse-grained (UCG) model of the SARS-CoV-2 virion from the authentic cryo-electron microscopy data, which enables MD simulation of the entire virion within microseconds. In addition, a hybrid all-atom and UCG (AA/UCG) virion model involving an all-atom spike protein is developed for the investigation of the spike protein interactions. A comparison of the conformational changes for the spike proteins as simulated in the hybrid model and that isolated in solution as in the free form reveals that the former is completely different from the latter. The simulation results demonstrate the necessity for the development of multiscale models to study the functions of proteins in the biomolecular complexes.

A rapid and highly sensitive multiple detection of human adenovirus type 3, type 7 and respiratory syncytial virus by recombinase-aided reverse transcription PCR.

BACKGROUND: Polymerase chain reaction (PCR) has been widely used for many pathogen detection. However, PCR technology still suffers from long detection time and insufficient sensitivity. Recombinase-aided amplification (RAA) is a powerful nucleic acid detection tool with high sensitivity and amplification efficiency, but its complex probes and inability of multiplex detection hinder the further application of this technology. METHODS: In this study, we developed and validated the multiplex reverse transcription recombinase-aided PCR (multiplex RT-RAP) assay for human adenovirus 3 (HADV3), human adenovirus 7 (HADV7), and human respiratory syncytial virus (HRSV) within 1 h with Human RNaseP protein as a reference gene to monitor the whole process. RESULTS: Using recombinant plasmids, the sensitivity of multiplex RT-RAP for the detection of HADV3, HADV7, and HRSV was 18, 3, and 18 copies per reaction, respectively. The multiplex RT-RAP showed no cross-reactivity with other respiratory viruses, demonstrating its good specificity. A total of 252 clinical specimens were tested by multiplex RT-RAP and the results were found to be consistent with those of corresponding RT-qPCR assays. After testing serial dilutions of selected positive specimens, the detection sensitivity of multiplex RT-RAP was two to eightfold higher than that of corresponding RT-qPCR. CONCLUSION: We conclude the multiplex RT-RAP is a robust, rapid, highly sensitive, and specific assay with the potential to be used in the screening of clinical samples with low viral load.

Rapid two-stage amplification in a single tube for simultaneous detection of norovirus GII and group a rotavirus.

The most prevalent viruses currently causing diarrhea are norovirus and rotavirus, and rapid and sensitive detection methods are essential for the early diagnosis of disease. The purpose of this study was to establish a sensitive single-tube two-stage nucleic acid amplification method-reverse transcription recombinase-assisted PCR (RT-RAP)-for simultaneous detection of norovirus GII and group A Rotavirus, with the first stage consisting of isothermal reverse transcription recombinase-aided amplification (RT-RAA) and the second stage consisting of qPCR (quantitative PCR). RT-RAP is more sensitive than either RT-RAA or qRT-PCR (quantitative RT-PCR) alone. And the addition of a barrier that can be disassembled after heating enabled the detection of samples within 1 h in a single closed tube. Sensitivity was 10 copies/reaction of norovirus (Novs) GII and group A rotavirus (RVA). In parallel, two hundred fecal specimens were used to evaluate the method and compare it with a commercial fluorescent quantitative RT-PCR. The data showed kappa values of 0.957 and 0.98 (p < 0.05) for detecting Novs GII and RVA by the two methods, indicating the potential of the newly established assay to be applied to clinical and laboratory testing.

Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification.

The complexity and multivariate analysis of biological systems and environment are the drawbacks of the current high-throughput sensing method and multianalyte identification. Deep learning (DL) algorithms contribute a big advantage in analyzing the nonlinear and multidimensional data. However, most DL models are data-driven black boxes suffering from nontransparent inner workings. In this work, we developed an explainable DL-assisted visualized fluorometric array-based sensing method. Based on a data set of 8496 fluorometric images of various target molecule fingerprint patterns, two typical DL algorithms and eight machine learning algorithms were investigated for the efficient qualitative and quantitative analysis of six aminoglycoside antibiotics (AGs). The convolutional neural network (CNN) approached 100% prediction accuracy and 1.34 ppm limit of detection of six AG analysis in domestic, industrial, medical, consumption, or aquaculture water. The class activation mapping assessment explicates how the CNN model assesses the importance of sensor elements and makes the discrimination decision. The feedback mechanism guides the sensor array evolution for less material using a simplified operation or efficient data acquisition. The explainable DL-assisted analysis method establishes an "end-to-end" strategy to resolve the black box of the DL algorithm, promote hardware design or principle optimization, and contribute facile indicators for environment monitoring, disease diagnosis, and even new scientific discovery.

Integrated Aptasensor Array for Sweat Drug Analysis.

Drug abuse is seriously endangering human health and jeopardizing society. There is an urgent need for rapid, sensitive, portable, and easy-to-operate methods for the daily detection of drugs in biological matrices. However, current drug detection methods based on chromatography, spectroscopy, immunosorbent assays, etc. are limited by the requirements of high logistical instruments and laboratory. Herein, we proposed a wearable electrochemical aptasensor with high sensitivity and specificity for the direct capture and rapid detection of multiple drugs in sweat. The single aptamer and dual aptamers with different base compositions were designed to compose the aptasensor array. Molecular docking simulations demonstrated different binding affinities between bioamines and aptamers. The developed aptasensor array is shown to be sufficient to generate distinct electrochemical fingerprints for different psychoactive drugs and interfering substances by extracting variable features from electrochemical signals. Sixteen analytes in the same concentration or gradient concentrations were identified with 100% accuracy. In addition, the wearable sensor platform was demonstrated to discriminate various drugs with similar chemical structures in artificial sweat and human sweat samples. The sensor array not only provided a new rapid method for the detection of drugs but also served as a reference for developing wearable sensors for onsite and daily testing of human biochemical information.

Elastic-Electric Coefficient-Sensitive Hydrogel Sensors toward Sweat Detection.

The complex and multivariate biological systems and environment are challenging the development of related detection and analysis. It calls for the multiresponsive and facile sensing material and method for multi-analyte identification. In this work, we proposed an elastic-electric coefficient sensitivity strategy with hydrogel [amino trimethylene phosphonic acid-assisted poly(vinyl alcohol)] to achieve discriminative analysis of various chemicals. Elastic sensitivity based on the Hofmeister effect and electric sensitivity based on hydrated ion migration are explored in detail. With a rational design, the elastic-electric coefficient-sensitive hydrogel can qualify and quantify various kinds of chemicals (cations, anions, amino acids, saccharides, and lactate). The facile hydrogel sensor realized complicated sweat recognition and can be used in various applications such as environment monitoring, disease diagnosis, and athletic training optimization.

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image analysis has been demonstrated to be an efficient big data process which shows accurate individual identification. However, the data-driven "black boxes" of current DL algorithms are suffering from the nontransparent inner working mechanism. In this work, we designed a programmable colorimetric chip with explainable DL to approach accurate classification and quantification analysis of sweat samples. Gel (sodium alginate) capsules with different indicators were adopted to combinate as designed programmable colorimetric chips. We collected 4600 colorimetric response images as the data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Glucose, pH, and lactate in human sweat could be facilely and 100% accurately classified and quantified by the convolutional neural network (CNN) DL algorithm, and the testing results of actual sweat via the DL-assisted colorimetric approach match 91.0-99.7% with the laboratory measurements. Class activation mapping (CAM) was processed to visualize the inner working mechanism of CNN operation, which could help to verify and explicate the design rationality of colorimetric chips. The explainable DL-assisted programmable colorimetric chip provided an "end-to-end" strategy to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for clinical monitoring, disease prevention, and even new scientific discoveries.

Metal dimers embedded vertically in defect-graphene as gas sensors: a first-principles study.

A highly symmetric structure of metal dimers embedded in defect-graphene (M2⊥gra) in a perpendicular manner was designed. Five M2⊥gra (M = Co, Ni, Rh, Ir and Pt) monolayers were identified to be stable by density functional theory (DFT) calculations. To investigate the capability of those new structures as gas sensors, the adsorption behavior of ten gas molecules (O2, N2, CO, CO2, NO, NO2, NH3, H2O, H2S and SO2) on M2⊥gra was explored, and the charge transfer, magnetism changes, etc. of these adsorption systems were analyzed. The Ni2⊥gra can be used as a gas sensor for O2 at 500 K by the analysis of electronic and magnetic properties. At room temperature, the Pt2⊥gra is expected to be an excellent CO2 gas selector due to its high selectivity, sensitivity and short recovery time (1.04 × 10-12 s). The electronic and magnetic coupling between the metal atoms in the vertical metal dimers plays an important role in sensing gas molecules. Our work paves a new way to design metal-dimer-based nanomaterials.

B96: a complete core-shell structure with high symmetry.

A complete core-shell and highly symmetric B96 was designed. The core-shell B96 of Th symmetry is energetically favorable compared to the bilayer motif and the core-shell structure can be well maintained during first-principles molecular dynamics simulations at high temperatures (up to 1000 K). Moreover, it exhibits a superatomic electronic configuration and spherical aromaticity. Our theoretical work not only confirmed that the core-shell structural pattern is more energetically favorable for large-sized boron clusters, but also provided a strategy to design large boron clusters with a core-shell structure of high symmetry.