Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields

Heavy-Metal-Free Colloidal Quantum Dots: Progress and Opportunities in Solar Technologies

Colloidal quantum dots (QDs) hold great promise as building blocks in solar technologies owing to their remarkable photostability and adjustable properties through the rationale involving size, atomic composition of core and shell, shapes, and surface states. However, most high-performing QDs in solar conversion contain hazardous metal elements, including Cd and Pb, posing significant environmental risks. Here, a comprehensive review of heavy-metal-free colloidal QDs for solar technologies, including photovoltaic (PV) devices, solar-to-chemical fuel conversion, and luminescent solar concentrators (LSCs), is presented. Emerging synthetic strategies to optimize the optical properties by tuning the energy band structure and manipulating charge dynamics within the QDs and at the QDs/charge acceptors interfaces, are analyzed. A comparative analysis of different synthetic methods is provided, structure-property relationships in these materials are discussed, and they are correlated with the performance of solar devices. This work is concluded with an outlook on challenges and opportunities for future work, including machine learning-based design, sustainable synthesis, and new surface/interface engineering.

Synergistic Effects of Amine Functional Groups and Enriched-Atomic-Iron Sites in Carbon Dots for Industrial-Current-Density CO2 Electroreduction

Metal phthalocyanine molecules with Me-N4 centers have shown promise in electrocatalytic CO2 reduction (eCO2R) for CO generation. However, iron phthalocyanine (FePc) is an exception, exhibiting negligible eCO2R activity due to a higher CO2 to *COOH conversion barrier and stronger *CO binding energy. Here, amine functional groups onto atomic-Fe-rich carbon dots (Af-Fe-CDs) are introduced via a one-step solvothermal molecule fusion approach. Af-Fe-CDs feature well-defined Fe-N4 active sites and an impressive Fe loading (up to 8.5 wt%). The synergistic effect between Fe-N4 active centers and electron-donating amine functional groups in Af-Fe-CDs yielded outstanding CO2-to-CO conversion performance. At industrial-relevant current densities exceeding 400 mA cm-2 in a flow cell, Af-Fe-CDs achieved >92% selectivity, surpassing state-of-the-art CO2-to-CO electrocatalysts. The in situ electrochemical FTIR characterization combined with theoretical calculations elucidated that Fe-N4 integration with amine functional groups in Af-Fe-CDs significantly reduced energy barriers for *COOH intermediate formation and *CO desorption, enhancing eCO2R efficiency. The proposed synergistic effect offers a promising avenue for high-efficiency catalysts with elevated atomic-metal loadings.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

Carbon dots as versatile nanomaterials in sensing and imaging: Efficiency and beyond

Carbon dots (CDs) have emerged as a versatile and promising carbon-based nanomaterial with exceptional optical properties, including tunable emission wavelengths, high quantum yield, and photostability. CDs are appropriate for various applications with many benefits, such as biocompatibility, low toxicity, and simplicity of surface modification. Thanks to their tunable optical properties and great sensitivity, CDs have been used in sensing as fluorescent probes for detecting pH, heavy metal ions, and other analytes. In addition, CDs have demonstrated potential as luminescence converters for white organic light-emitting diodes and light emitters in optoelectronic devices due to their superior optical qualities and exciton-independent emission. CDs have been used for drug administration and bioimaging in the biomedical field due to their biocompatibility, low cytotoxicity, and ease of functionalization. Additionally, due to their stability, efficient charge separation, and low recombination rate, CDs have shown interesting uses in energy systems, such as photocatalysis and energy conversion. This article highlights the growing possibilities and potential of CDs as adaptable nanomaterials in a variety of interdisciplinary areas related to sensing and imaging, at the same time addressing the major challenges involved in the current research and proposing scientific solutions to apply CDs in the development of a super smart society.

Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots

Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous synthesis parameters and multiple desired outcomes, creating an enormous search space. In this study, we present a novel multi-objective optimization strategy utilizing a machine learning (ML) algorithm to intelligently guide the hydrothermal synthesis of CQDs. Our closed-loop approach learns from limited and sparse data, greatly reducing the research cycle and surpassing traditional trial-and-error methods. Moreover, it also reveals the intricate links between synthesis parameters and target properties and unifies the objective function to optimize multiple desired properties like full-color photoluminescence (PL) wavelength and high PL quantum yields (PLQY). With only 63 experiments, we achieve the synthesis of full-color fluorescent CQDs with high PLQY exceeding 60% across all colors. Our study represents a significant advancement in ML-guided CQDs synthesis, setting the stage for developing new materials with multiple desired properties.

Carbonization of quercetin into nanogels: a leap in anticoagulant development

Quercetin, a flavonoid abundantly found in onions, fruits, and vegetables, is recognized for its pharmacological potential, especially for its anticoagulant properties that work by inhibiting thrombin and coagulation factor Xa. However, its clinical application is limited due to poor water solubility and bioavailability. To address these limitations, we engineered carbonized nanogels derived from quercetin (CNGsQur) using controlled pyrolysis and polymerization techniques. This led to substantial improvements in its anticoagulation efficacy, water solubility, and biocompatibility. We generated a range of CNGsQur by subjecting quercetin to varying pyrolytic temperatures and then assessed their anticoagulation capacities both in vitro and in vivo. Coagulation metrics, including thrombin clotting time (TCT), activated partial thromboplastin time (aPTT), and prothrombin time (PT), along with a rat tail bleeding assay, were utilized to gauge the efficacy. CNGsQur showed a pronounced extension of coagulation time compared to uncarbonized quercetin. Specifically, CNGsQur synthesized at 270 °C (CNGsQur270) exhibited the most significant enhancement in TCT, with a binding affinity to thrombin exceeding 400 times that of quercetin. Moreover, variants synthesized at 310 °C (CNGsQur310) and 290 °C (CNGsQur290) showed the most substantial delays in PT and aPTT, respectively. Our findings indicate that the degree of carbonization significantly influences the transformation of quercetin into various CNGsQur forms, each affecting distinct coagulation pathways. Additionally, both intravenous and oral administrations of CNGsQur were found to extend rat tail bleeding times by up to fivefold. Our studies also demonstrate that CNGsQur270 effectively delays and even prevents FeCl3-induced vascular occlusion in a dose-dependent manner in mice. Thus, controlled pyrolysis offers an innovative approach for generating quercetin-derived CNGs with enhanced anticoagulation properties and water solubility, revealing the potential for synthesizing self-functional carbonized nanomaterials from other flavonoids for diverse biomedical applications.

Isomerization Engineering of Oxygen-Enriched Carbon Quantum Dots for Efficient Electrochemical Hydrogen Peroxide Production

Hydrogen peroxide (H2O2) has emerged as a kind of multi-functional green oxidants with extensive industrial utility. Oxidized carbon materials exhibit promises as electrocatalysts in the two-electron (2e-) oxygen reduction reaction (ORR) for H2O2 production. However, the precise identification and fabrication of active sites that selectively yield H2O2 present a serious challenge. Herein, a structural engineering strategy is employed to synthesize oxygen-doped carbon quantum dots (o-CQD) for the 2e- ORR. The surface electronic structure of the o-CQDs is systematically modulated by varying isomerization precursors, thereby demonstrating excellent electrocatalyst performance. Notably, o-CQD-3 emerges as the most promising candidate, showcasing a remarkable H2O2 selectivity of 96.2% (n = 2.07) at 0.68 V versus RHE, coupled with a low Tafel diagram of 66.95 mV dec-1. In the flow cell configuration, o-CQD-3 achieves a H2O2 productivity of 338.7 mmol gcatalyst -1 h-1, maintaining consistent production stability over an impressive 120-hour duration. Utilizing in situ technology and density functional theory calculations, it is unveil that edge sites of o-CQD-3 are facilely functionalized by C-O-C groups under alkaline ORR conditions. This isomerization engineering approach advances the forefront of sustainable catalysis and provides a profound insight into the carbon-based catalyst design for environmental-friendly chemical synthesis processes.

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.

Calcium-enriched carbon nanoparticles loaded with indocyanine green for near-infrared fluorescence imaging-guided synergistic calcium overload, photothermal therapy, and glutathione-depletion-enhanced photodynamic therapy

Combining phototherapy with other treatments has significantly advanced cancer therapy. Here, we designed and fabricated calcium-enriched carbon nanoparticles (Ca-CNPs) that could effectively deplete glutathione (GSH) and release calcium ions in tumors, thereby enhancing the efficacy of photodynamic therapy (PDT) and the calcium overload effect that leads to mitochondrial dysfunction. Due to the electrostatic interaction, π-π stacking interaction, multiple hydrogen bonds, and microporous structures, indocyanine green (ICG) was loaded onto the surface of Ca-CNPs with a high loading efficiency of 44.7 wt%. The obtained Ca-CNPs@ICG can effectively improve the photostability of ICG while retaining its ability to generate singlet oxygen (1O2) and undergo photothermal conversion (Ca-CNPs@ICG vs. ICG, 45.1% vs. 39.5%). In vitro and in vivo experiments demonstrated that Ca-CNPs@ICG could be used for near-infrared fluorescence imaging-guided synergistic calcium overload, photothermal therapy, and GSH depletion-enhanced PDT. This study sheds light on the improvement of 1O2 utilization efficiency and calcium overload-induced mitochondrial membrane potential imbalance in tumor cells.

Perspectives on development of optoelectronic materials in artificial intelligence age

Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.

Carbon Dots with Antioxidant Capacity for Detecting Glucose by Fluorescence and Repairing High-Glucose Damaged Glial Cells

Diabetic mellitus management extends beyond blood glucose monitoring to the essential task of mitigating the overexpression of reactive oxygen species (ROS), particularly vital for cellular repair, especially within the nervous system. Herein, antioxidant carbon dots (Arg-CDs) were designed and prepared using anhydrous citric acid, L-arginine, and ethylenediamine as sources through a hydrothermal method. Arg-CDs exhibited excellent scavenging ability to 2,2-Diphenyl-1-picrylhydrazyl (DPPH∙), and fluorescence response to hydroxyl radicals (∙OH), a characteristic representative of reactive oxygen species (ROS). Assisted by glucose oxidase and Fe2+, Arg-CDs showed a sensitive and selective response to glucose. The quenching mechanism of Arg-CDs by formed ∙OH was based on the static quenching effect (SQE). The analytical performance of this method for glucose detection encompassed a wide linear range (0.3-15 μM), a low practical limit of detection (0.1 μM) and practical applicability for blood glucose monitoring. In an in vitro model employing glial cells (BV2 cells), it was observed that high glucose medium led to notable cellular damage ascribed to the excessive ROS production from hyperglycemia. The diminished and apoptotic glial cells were gradually recovered by adding increased contents of Arg-CDs. This work illustrates a promising area that designs effective carbon dots with antioxidant capacity for the dual applications of detection and cell repairing based on the utilization of antioxidant activity.

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.

Facile preparation strategy of novel B2O3-modified carbon dots with 1.99 s ultra-long Room-Temperature phosphorescence for multidimensional encryption

Facile synthesis of Ultralong room-temperature phosphorescence (URTP) with super stability and long-afterglow are of great significance, but hard to achieve. Herein, a brilliant gram-scale and solvent-free pyrolysis treatment strategy has been developed to prepare high-performance URTP carbon dots (CDs) by regulating different temperature (250-500 °C). The optimized CDs (CD-400) showed room-temperature phosphorescence 1.99 s and lasting over 22 s to naked eyes, which is superior to most of the reported URTP CDs. Owing to the stabilization effects of the modified B2O3 layer on the surface, the homogenous distribution of CD-400 with the narrow diameter of 1.44 nm was constructed, displaying a superb stability through hydrogen-bond network. In addition, the doping atoms (N, O) greatly enhanced the n-π* transitions and stabilized triplet excitons radiative transitions, facilitating the effective intersystem crossing (ISC) and the RTP emissions. More importantly, the B2O3-modified CDs were successfully applied in the multi-level information encryption (time-resolved RTP performance) and fingerprint identification (bifurcation, whorl and termination details).

Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring

Long-term continuous water quality monitoring (LTCM) is crucial to ensure the safety of water resources. However, lab-based pollutant detection via machine learning (ML) usually involves colorimetric materials or sensors, and it cannot be ignored that sensor limitations prevent their use for LTCM. To address this challenge, we propose a novel method that leverages image recognition to establish a relationship between pollutant concentration and color. By extracting efficient color variation features from raw pixel matrices using a combination of Kmeans clustering and RGB average features, the concentrations of pollutants that are difficult to distinguish by the naked eyes can be directly captured without the need for sensors and preprocessing. Four ML models (XGBoost, Linear, support vector regression (SVR), and Ridge) achieved up to a 95.9% increase in coefficient of determination (R2) compared to principal component analysis (PCA). In the prediction of the concentration of simulated pollutants such as Cu2+, Co2+, Rhodamine B, and the concentration of Cr(VI) in actual electroplating wastewater, natural resource water and drinking water, over 95% R2 was achieved. The method reported in our work can effectively capture subtle color changes that cannot be observed by the naked eyes without any preprocessing of water samples, providing a reliable method for LTCM.

Carbon Dots: From Synthesis to Unraveling the Fluorescence Mechanism

Carbon dots (CDs) being a new type of carbon-based nanomaterial have attracted intensive interest from researchers owing to their excellent biophysical properties. CDs are a class of fluorescent carbon nanomaterials that have emerged as a promising alternative to traditional quantum dots and organic dyes in applications including bioimaging, sensing, and optoelectronics. CDs possess unique optical properties, such as tunable emission, facile synthesis, and low toxicity, making them attractive for many applications in biology, medicine, and environmental areas. The synthesis of CDs is achievable by a variety of methods, including bottom-up and top-down approaches, involving the use of different carbon sources and surface functionalization strategies. However, understanding the fluorescence mechanism of CDs remains a challenge. Various mechanistic models have been proposed to explain their origin of luminescence. This review summarizes the recent developments in the synthesis and functionalization of CDs and provides an overview of the current understanding of the fluorescence mechanism.

Machine-Learning-Driven G-Quartet-Based Circularly Polarized Luminescence Materials

Circularly polarized luminescence (CPL) materials have garnered significant interest due to their potential applications in chiral functional devices. Synthesizing CPL materials with a high dissymmetry factor (glum ) remains a significant challenge. Inspired by efficient machine learning (ML) applications in scientific research, this work demonstrates ML-based techniques for the first time to guide the synthesis of G-quartet-based CPL gels with high glum values and multiple chiral regulation strategies. Employing an "experiment-prediction-verification" approach, this work devises a ML classification and regression model for the solvothermal synthesis of G-quartet gels in deep eutectic solvents. This process illustrates the relationship between various synthesis parameters and the glum value. The decision tree algorithm demonstrates superior performance across six ML models, with model accuracy and determination coefficients amounting to 0.97 and 0.96, respectively. The screened CPL gels exhibiting a glum value up to 0.15 are obtained through combined ML guidance and experimental verification, among the highest ones reported till now for biomolecule-based CPL systems. These findings indicate that ML can streamline the rational design of chiral nanomaterials, thereby expediting their further development.

Gram-Scale Mechanochemical Synthesis of Atom-Layer MoS2 Semiconductor Electrocatalyst via Functionalized Graphene Quantum Dots for Efficient Hydrogen Evolution

The development of advanced and efficient synthetic methods is pivotal for the widespread application of 2D materials. In this study, a facile and scalable solvent-free mechanochemical approach is approached, employing graphene quantum dots (GQDs) as exfoliation agents, for the synthesis and functionalization of nearly atom-layered MoS2 nanosheets (ALMS). The resulting ALMS exhibits an ultrathin average thickness of 4 nm and demonstrates high solvent stability. The impressive yield of ALMS reached 63%, indicating its potential for scalable production of stable nanosheets. Remarkably, the ALMS catalyst exhibits excellent HER performance. Moreover, the ALMS catalyst showcases exceptional long-term durability, maintaining stable performance for nearly 200 h, underscoring its potential as a highly efficient and durable electrocatalyst. Significantly, the catalytic properties of ALMS are significantly influenced by ball milling production conditions. The GQD-assisted large-scale machinery synthesis pathway provides a promising avenue for the development of efficient and high-performance ultrathin 2D materials.

Functionalized heteroatom-doped carbon dots for biomedical applications: A review

Carbon nanoparticles (CDs) have recently drawn a great attention in (bio)chemical analysis, sensing and bioimaging owing to their photostability, water stability, minimal toxicity, biocompatibility and ease of surface functionalization. While the vast majority of CDs applications rely on exploiting their fluorescent properties, doping such nanomaterials with various elements has recently received increasing attention as an effective approach to modify their optoelectronic characteristics, introducing novel improved optical features such as phosphorescence, upconversion luminescence or multimodal imaging capabilities. This review article focuses in the recent advances on the synthesis of heteroatom-doped CDs, exhibiting distinctive features of high value for sensing and imaging, as well as various functionalization schemes developed for guided analyte labeling. Relevant applications in chemical sensing, bioimaging and disease therapy are here presented. A final section intends to provide an overview towards future developments of such emerging light-emitting nanomaterials in the design of future devices and strategies for (bio)analytical chemistry.

High-Performance Fluorescent Microspheres Based on Fluorescence Resonance Energy Transfer Mode for Lateral Flow Immunoassays

The label with a large Stokes shift and strong fluorescence properties could improve the sensitivity of the lateral flow immunoassay (LFIA). Herein, two aggregation-induced emission (AIE) luminogens with spectral overlap were encapsulated in polymers by using the microemulsion method as a label, and the construction of a fluorescence resonance energy transfer mode was further verified via theoretical calculation and spectral analysis. Satisfactorily, the doped AIE polymer microspheres (DAIEPMs) exhibited a large Stokes shift of 285 nm and a 10.8-fold fluorescence enhancement compared to those of the AIEPMs loaded with acceptors. Benefiting from the excellent optical performance, DAIEPMs were applied to the LFIA for sensitive detection of chlorothalonil, which is an organochlorine pesticide. The limit of detection of the proposed DAIEPMs-LFIA was 1.2 pg/mL, which was 4.8-fold and 11.6-fold lower than those of quantum dot bead LFIA and gold nanoparticle LFIA, respectively. This work provides a new strategy to improve the optical properties of fluorescent materials and construct a sensitive and reliable detection platform.

Machine learning predicted emission of water-stable CdTe quantum dots

Quantum dots (QDs) have attracted much attention and exhibit many attractive properties, including high absorption coefficient, adjustable bandgap, high brightness, long-term stability, and size-dependent emission. It is known that to obtain high-quality luminescent properties (i.e. emission color, color purity, quantum yield, and stability), the synthesis parameters must be precisely controlled. In this work, we have constructed a database with CdTe aqueous synthesis parameters and spectroscopic results and applied machine learning algorithms to better understand the influence of the main synthesis parameters of CdTe QDs on their final emission properties. A strong dependence of the final emission wavelength with the reaction time and surface ligands and precursors concentrations was demonstrated. These parameters adjusted synchronously were shown to be very useful for provide ideal synthesis conditions for the preparation of CdTe QDs with desirable emission wavelengths. Moreover, applying the algorithms correctly allows for obtaining information and insights into the growth kinetics of QDs under different synthetic conditions.

Amide Covalent Bonding Engineering in Heterojunction for Efficient Solar-Driven CO2 Reduction

Inefficient charge separation and slow interfacial reaction dynamics significantly hamper the efficiency of photocatalytic CO2 reduction. Herein, a facile EDC/NHS-assisted linking strategy was developed to enhance charge separation in heterojunction photocatalysts. Using this approach, we successfully synthesized amide-bonded carbon quantum dot-g-C3N4 (CQD-CN) heterojunction photocatalysts. The formation of amide covalent bonds between CN and CQDs in the CN-CQD facilitates efficient carrier migration, CO2 adsorption, and activation. Exploiting these advantages, the CN-CQD photocatalysts exhibit high selectivity with CO and CH4 evolution rates of 79.2 and 2.7 μmol g-1 h-1, respectively. These rates are about 1.7 and 3.6 times higher than those of CN@CQD and bulk CN, respectively. Importantly, the CN-CQD photocatalysts demonstrate exceptional stability, even after 12 h of continuous testing. The presence of the COOH* signal is identified as a crucial intermediate species in the conversion of CO2 to CO. This study presents a covalent bonding engineering strategy for developing high-performance heterojunction photocatalysts for efficient solar-driven reduction of CO2.

Machine-learning analysis to predict the fluorescence quantum yield of carbon quantum dots in biochar

Biochar nanoparticles have recently attracted attention, owing to their environmental behavior and ecological effects. However, biochar has not been shown to contain carbon quantum dots (< 10 nm) with unique photovoltaic properties. Therefore, this study utilized several characterization techniques to demonstrate the generation of carbon quantum dots in biochar produced from 10 types of farm waste. The generated carbon quantum dots had a quasi-spherical morphology and high-resolution lattice stripes with lattice spacings of 0.20-0.23 nm. Moreover, they contained functional groups with good hydrophilic properties, such as amino and hydroxyl groups, and elemental O, C, and N on the surface. A crucial determinant of the photoluminescence properties of carbon quantum dots is their fluorescence quantum yield. Therefore, the relationship between the biochar preparation parameters and the fluorescence quantum yield was investigated using six machine learning analytical models based on 480 samples. Among the models, the gradient-boosting decision-tree regression model exhibited the best predictive performance (R2 > 0.9, RMSE <0.02, and MAPE <3), and was used for the analysis of feature importance; compared to the properties of the raw material, the production parameters had a greater effect on the fluorescence quantum yield. Additionally, four key features were identified: pyrolysis temperature, residence time, N content, and C/N ratio, which were independent of farm waste type. These features can be used to accurately predict the fluorescence quantum yield of carbon quantum dots in biochar. The relative error range between the predicted and the experimental value of fluorescence quantum yield is 0.00-4.60 %. Thus, the prediction model has the potential to predict the fluorescence quantum yield of carbon quantum dots in other types of farm waste biochar, and provides fundamental information for the study of biochar nanoparticles.

Solvent Engineering of Oxygen-Enriched Carbon Dots for Efficient Electrochemical Hydrogen Peroxide Production

The development of cost-effective and reliable metal-free carbon-based electrocatalysts has gained significant attention for electrochemical hydrogen peroxide (H2 O2 ) generation through a two-electron oxygen reduction reaction. In this study, a scalable solvent engineering strategy is employed to fabricate oxygen-doped carbon dots (O-CDs) that exhibit excellent performance as electrocatalysts. By adjusting the ratio of ethanol and acetone solvents during the synthesis, the surface electronic structure of the resulting O-CDs can be systematically tuned. The amount of edge active CO group was strongly correlated with the selectivity and activity of the O-CDs. The optimum O-CDs-3 exhibited extraordinary H2 O2 selectivity of up to 96.55% (n = 2.06) at 0.65 V (vs RHE) and achieved a remarkably low Tafel plot of 64.8 mV dec-1 . Furthermore, the realistic H2 O2 productivity yield of flow cell is measured to be as high as 111.18 mg h-1  cm-2 for a duration of 10 h. The findings highlight the potential of universal solvent engineering approach for enabling the development of carbon-based electrocatalytic materials with improved performance. Further studies will be undertaken to explore the practical implications of the findings for advancing the field of carbon-based electrocatalysis.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Phosphorescence, fluorescence, and colorimetric triple-mode sensor for the detection of acid phosphatase and corresponding inhibitor

Acid phosphatase (ACP) as a clinical diagnostic biomarker for several pathophysiological diseases has aroused widespread interest. Compared to commonly developed single-mode ACP detection technology, the multi-mode detection method with self-validation can provide more reliable results. Herein, we proposed a triple-mode phosphorescence, fluorescence, and colorimetric method for ACP detection in combination with CDs@SiO₂. HAuCl₄ with oxidase-like activity can catalyze the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to the blue oxide TMB (TMBox), offering absorption signals and quenching the phosphorescence and fluorescence of CDs@SiO₂ based on the internal filtration effect (IFE). ACP can hydrolyze ascorbic acid 2-phosphate (AAP) to yield ascorbic acid (AA), thereby reducing TMBox to TMB, triggering solution fading and restoring phosphorescence and fluorescence signals. When the ACP inhibitor malathion is present, the reduction of TMBox is hindered, which successively led to the suppression of CDs@SiO₂ phosphorescence and fluorescence signal recovery. According to these principles, triple-mode ACP (LOD = 0.0026 mU mL^-1) and malathion detections (LOD = 0.039 μg mL^-1) with favorable accuracy and sensitivity are realized. With simplicity, robustness, and versatility, the triple-mode sensor can be extended to the detection of the AAP hydrolase family and the screening of corresponding inhibitors.

The Detection of Anthrax Biomarker DPA by Ratiometric Fluorescence Probe of Carbon Quantum Dots and Europium Hybrid Material Based on Poly(ionic)- Liquid

Bacillus anthracis has gained international attention as a deadly bacterium and a potentially deadly biological warfare agent. Dipicolinic acid (DPA) is the main component of the protective layer of anthracis spores, and is also an anthrax biomarker. Therefore, it is of great significance to explore an efficient and sensitive DPA detection method. Herein, a novel ratio hybrid probe (CQDs-PIL-Eu3+) was prepared by a simple one-step hydrothermal method using carbon quantum dots (CQDs) as an internal reference fluorescence and a covalent bond between CQDs and Eu3+ by using a polyionic liquid (PIL) as a bridge molecule. The ratiometric fluorescence probe was found to have the characteristics of sensitive fluorescence visual sensing in detecting DPA. The structure and the sensing properties of CQDs-PIL-Eu3+ were investigated in detail. In particular, the fluorescence intensity ratio of Eu3+ to CQDs (I616/I440) was linear with the concentration of DPA in the range of 0-50 μM, so the detection limit of the probe was as low as 32 nm, which was far lower than the DPA dose released by the number of anthrax spores in human body (60 μM) and, thus, can achieve sensitive detection. Therefore, the ratiometric fluorescence probe in this work has the characteristics of strong anti-interference, visual sensing, and high sensitivity, which provides a very promising scheme for the realization of anthrax biomarker DPA detection.

Magnetic Nanozyme Based on Loading Nitrogen-Doped Carbon Dots on Mesoporous Fe3O4 Nanoparticles for the Colorimetric Detection of Glucose

The simple and accurate monitoring of blood glucose level is of great significance for the prevention and control of diabetes. In this work, a magnetic nanozyme was fabricated based on loading nitrogen-doped carbon dots (N-CDs) on mesoporous Fe₃O₄ nanoparticles for the colorimetric detection of glucose in human serum. Mesoporous Fe₃O₄ nanoparticles were easily synthesized using a solvothermal method, and N-CDs were then prepared in situ and loaded on the Fe₃O₄ nanoparticles, leading to a magnetic N-CDs/Fe₃O₄ nanocomposite. The N-CDs/Fe₃O₄ nanocomposite exhibited good peroxidase-like activity and could catalyze the oxidation of the colorless enzyme substrate 3,3',5,5'-tetramethylbenzidine (TMB) to blue TMB oxide (ox-TMB) in the presence of hydrogen peroxide (H₂O₂). When the N-CDs/Fe₃O₄ nanozyme was combined with glucose oxidase (Gox), Gox catalyzed the oxidization of glucose, producing H₂O₂ and leading to the oxidation of TMB under the catalysis of the N-CDs/Fe₃O₄ nanozyme. Based on this mechanism, a colorimetric sensor was constructed for the sensitive detection of glucose. The linear range for glucose detection was from 1 to 180 μM, and the limit of detection (LOD) was 0.56 μM. The recovered nanozyme through magnetic separation showed good reusability. The visual detection of glucose was also realized by preparing an integrated agarose hydrogel containing the N-CDs/Fe₃O₄ nanozyme, glucose oxidase, and TMB. The colorimetric detection platform has an enormous potential for the convenient detection of metabolites.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Carbon Dots for the Treatment of Inflammatory Diseases: An Appraisal of In Vitro and In Vivo Studies

In recent decades, several studies demonstrating various applications of carbon dots (C-dots), including metal sensing, bioimaging, pH sensing, and antimicrobial activities, have been published. Recent developments have shifted this trend toward biomedical applications that target various biomarkers relevant to chronic diseases. However, relevant developments and research results regarding the anti-inflammatory properties of C-dots against inflammation-associated diseases have not been systematically reviewed. Hence, this review discusses the anti-inflammatory effects of C-dots in in vivo and in vitro models of LPS-induced inflammation, gout, cartilage tissue engineering, drug-induced inflammation, spinal cord injury, wound healing, liver diseases, stomach cancer, gastric ulcers, acute kidney and lung injury, psoriasis, fever or hypothermia, and bone tissue regeneration. The compiled studies demonstrate the promising potential of C-dots as anti-inflammatory agents for the development of new drugs.

Surface state engineering of carbon dot/carbon nanotube heterojunctions for boosting oxygen reduction performance

Platinum-based (Pt) catalysts are the most common commercial catalysts for oxygen reduction reactions (ORR). Unfortunately, their high price, scarcity and poor durability hinder their further development. Therefore, the development of effective and economical ORR electrocatalysts has received increasing attention. Here, carbon dots (CDs) enriched in amino functional groups were successfully loaded onto carbon nanotubes (CNTs) with a large surface area and helical structure through a surface state engineering strategy. The resulting composites (CD/CNTs) are 0D/1D nano heterojunction structures. The CD/CNTs showed superior ORR activity compared with CNTs and CDs (E_oneset = 0.95 V, E_1/2 = 0.81 V and limiting current density = 4.74 mA cm^-2). In addition, the stability of CD/CNTs in an alkaline medium was up to 30000 s. The excellent ORR performance of CD/CNTs can be attributed to the dominant role of amino-N, the synergistic effect of heterojunctions formed by CDs and CNTs, and the high Lewis basicity. The composite electrocatalyst synthesized by the CD-regulated CNT strategy is expected to be a reliable cathode candidate for future energy conversion devices.

Hydrophobic Carbon Dots Derived from Organic Pollutants and Applications in NIR Anticounterfeiting and Bioimaging

In an effort to fulfill the strategy of sustainable development, Rhodamine B, a common and toxic organic pollutant in the textile industry, was reported for the first time as a single precursor to develop a kind of novel hydrophobic nitrogen-doped carbon dot (HNCD) through a green and facile one-pot solvothermal method. The HNCDs with an average size of 3.6 nm possess left and right water contact angles of 109.56° and 110.34°, respectively. The HNCDs manifest excitation wavelength-tunable and upconverted fluorescence from the ultraviolet (UV) to the near-infrared (NIR) range. Furthermore, the PEGylation of HNCDs enables them to be used as an optical marker for cell and in vivo imaging. Notably, the HNCDs with solvent-dependent fluorescence can be used for invisible inks with a wide range of light responses from UV-vis-NIR spectra. This work not only provides an innovative way to recycle chemical waste but also expands the potential application of HNCDs in NIR security printing and bioimaging.

Carbon Dots Based Photoinduced Reactions: Advances and Perspective

Seeking clean energy as an alternative to traditional fossil fuels is the inevitable choice to realize the sustainable development of the society. Photocatalytic technique is considered a promising energy conversion approach to store the abundant solar energy into other wieldy energy carriers like chemical energy. Carbon dots, as a class of fascinating carbon nanomaterials, have already become the hotspots in numerous photoelectric researching fields and particularly drawn keen interests as metal-free photocatalysts owing to strong UV-vis optical absorption, tunable energy-level configuration, superior charge transfer ability, excellent physicochemical stability, facile fabrication, low toxicity, and high solubility. In this review, the classification, microstructures, general synthetic methods, optical and photoelectrical properties of carbon dots are systematically summarized. In addition, recent advances of carbon dots based photoinduced reactions including photodegradation, photocatalytic hydrogen generation, CO₂ conversion, N₂ fixation, and photochemical synthesis are highlighted in detail, deep insights into the roles of carbon dots in various systems combining with the photocatalytic mechanisms are provided. Finally, several critical issues remaining in photocatalysis field are also proposed.

One-Step Synthesis of White-Light-Emitting Carbon Dots for White LEDs with a High Color Rendering Index of 97

White-light-emitting carbon dots (WCDs) show innate advantages as phosphors in white light-emitting diodes (WLEDs). For WLEDs, the color rendering index (CRI) is the most important metric to evaluate its performance. Herein, WCDs are prepared by a facile one-step solvothermal reaction of trimellitic acid and o-phenylenediamine. It consists of four CDs identified by column chromatography as blue, green, yellow, red, and thus white light is a superposition of these four types of light. The mixture of the four CDs undergoes Förster resonance energy transfer to induce the generation of white light. The photoluminescence of WCDs originates from the synergistic effect of carbon core and surface states. Thereinto, the carbon core states dominate in RCDs, and the increase of amide contents and degree of conjugation promote the redshift of the emission spectra, which is further confirmed by theoretical calculations. In addition, a high CRI of 97 is achieved when the WCDs are used as phosphors to fabricate WLEDs, which is almost the highest value up to now. The multicolor LEDs can also be fabricated by using the four multicolor CDs as phosphors, respectively. This work provides a novel approach to explore the rapid preparation of low-cost, high-performance WCDs and CDs-based WLEDs.

NBOH Site-Activated Graphene Quantum Dots for Boosting Electrochemical Hydrogen Peroxide Production

Carbon materials are considered promising 2/4 e^- oxygen reduction reaction (ORR) electrocatalysts for synthesizing H₂ O₂ /H₂ O via regulating heteroatom dopants and functionalization. Here, various doped and functionalized graphene quantum dots (GQDs) are designed to reveal the crucial active sites of carbon materials for ORR to produce H₂ O₂ . Density functional theory (DFT) calculations predict that the edge structure involving edge N, B dopant pairs and further OH functionalization to the B (NBOH) is an active center for 2e^- ORR. To verify the above predication, GQDs with an enriched density of NBOH (NBO-GQDs) are designed and synthesized by the hydrothermal reaction of NH₂ edge-functionalized GQDs with H₃ BO₃ forming six-member heterocycle containing the NBOH structure. When dispersed on conductive carbon substrates, the NBO-GQDs show H₂ O₂ selectivity of over 90% at 0.7 -0.8 V versus reversible hydrogen electrode in the alkaline solution in a rotating ring-disk electrode setup. The selectivity retains 90% of the initial value after 12 h stability test. In a flow cell setup, the H₂ O₂ production rate is up to 709 mmol g_catalyst ^-1  h^-1 , superior to most reported carbon- and metal-based electrocatalysts. This work provides molecular insight into the design and formulation of highly efficient carbon-based catalysts for sustainable H₂ O₂ production.

Machine Learning Predicting Optimal Preparation of Silica-Coated Gold Nanorods for Photothermal Tumor Ablation

Gold nanorods (GNRs) coated with silica shells are excellent photothermal agents with high surface functionality and biocompatibility. Understanding the correlation of the coating process with both structure and property of silica-coated GNRs is crucial to their optimizing preparation and performance, as well as tailoring potential applications. Herein, we report a machine learning (ML) prediction of coating silica on GNR with various preparation parameters. A total of 306 sets of silica-coated GNRs altogether were prepared via a sol-gel method, and their structures were characterized to extract a dataset available for eight ML algorithms. Among these algorithms, the eXtreme gradient boosting (XGboost) classification model affords the highest prediction accuracy of over 91%. The derived feature importance scores and relevant decision trees are employed to address the optimal process to prepare well-structured silica-coated GNRs. The high-throughput predictions have been adopted to identify optimal process parameters for the successful preparation of dumbbell-structured silica-coated GNRs, which possess a superior performance to a conventional cylindrical core-shell counterpart. The dumbbell silica-coated GNRs demonstrate an efficient enhanced photothermal performance in vivo and in vitro, validated by both experiments and time domain finite difference calculations. This study epitomizes the potential of ML algorithms combined with experiments in predicting, optimizing, and accelerating the preparation of core-shell inorganic materials and can be extended to other nanomaterial research.

Directional Charge Transfer Channels in a Monolithically Integrated Electrode for Photoassisted Overall Water Splitting

Photoelectrocatalytic performance of a system is fundamentally determined by the full absorption of sunlight and high utilization of photoexcited carriers, but efficiency of the latter is largely limited by inefficient charge transfer from the absorber to reactive sites. Here, we propose to construct directional charge transfer channels in a monolithically integrated electrode, taking carbon dots/carbon nitride (CCN) nanotubes and FeOOH/FeCo layered double hydroxide (FFC) nanosheets as a representative, to boost the photoassisted overall water splitting performance. Detailed experimental investigations and DFT calculations demonstrate that the interfacial C-O-Fe bonds between CCN and FFC act as charge transfer channels, facilitating the directional migration of the photogenerated carriers between CCN and FFC surfaces. Moreover, the in situ oxidized Fe/Co species by photogenerated holes trigger lattice oxygen activation, realizing the construction of the Fe-Co dual-site as the catalytic center and efficiently lowering the barrier energy for water oxidation. As a result, the CCN@FFC electrode shows multiple functionalities in photoelectrocatalysis: only a low overpotential of 68 mV, 182 mV, and 1.435 V is required to deliver 10 mA cm^-2 current densities for the photoassisted HER, OER, and overall water splitting, respectively. This directional charge transfer modulation strategy may facilitate the design of highly active and cost-effective multifunctional catalysts for energy conversion and storage.

Rational Design of Nitrogen-Doped Carbon Dots for Inhibiting β-Amyloid Aggregation

The fibrillization and abnormal aggregation of β-amyloid (Aβ) peptides are commonly recognized risk factors for Alzheimer's disease (AD) brain, and require an effective strategy to inhibit the Aβ deposition and treat AD. Herein, we designed and synthesized nitrogen-doped carbon dots (N-CDs) as an Aβ-targeted probe, which exhibits the capacity of inhibiting the 1-42 Aβ (Aβ1-42) self-assembly in vitro. The N-CDs exhibited orange emission with an emission wavelength of 570 nm, which demonstrates their excellent optical properties with excitation-independent behavior. Meanwhile, the N-CDs have spherical morphologies with an average size of 2.2 nm, whose surface enriches the amino, carboxyl, and hydroxyl groups. These preparties are conducive to improving their biological water solubility and provide a large number of chemical bonds for further interaction with proteins. Contrary to this, the kinetic process, size evolutions, and morphologies changes of Aβ1-42 were inhibited in the presence of N-CDs in the determination of a thioflavin T assay, dynamic light scattering, transmission electron microscope, etc. Finally, the safety application of N-CDs on Aβ1-42-induced cytotoxicity was further demonstrated via in vitro cytotoxicity experiments. This work demonstrates the effective outcome of suppressing Aβ aggregation, which provides a new view into the high-efficiency and low-cytotoxicity strategy in AD theranostics.

The synthesis of N-doped carbon dots for visual differentiating and detection of tetracyclines

N-doped carbon dots (N-CDs) were synthesized from L-glutamine and triethanolamine using a one-step hydrothermal method. The N-CDs emitting blue fluorescence had selective responses to tetracyclines (TCs) and could be used as a fluorescent probe to realize the quantitative detection and qualitative analysis of TCs. A method for the determination of TCs using the N-CDs in actual samples was successfully established. The recovery rate was maintained at 97.50-105.60%, and the relative standard deviation (RSD) was less than 3%. In addition, TCs can be visually distinguished using filter paper by the different fluorescence colours (light green, dark blue, and yellow-green) of the N-CDs/TCs system under ultraviolet light. This study provides a relatively simple method to detect and identify TCs.

Fabrication of Carbon-Based Quantum Dots via a "Bottom-Up" Approach: Topology, Chirality, and Free Radical Processes in "Building Blocks"

The discovery of carbon-based quantum dots (CQDs) has allowed opportunities for fluorescence bioimaging, tumor diagnosis and treatment, and photo-/electro-catalysis. Nevertheless, in the existing reviews related to the "bottom-up" approaches, attention is mainly paid to the applications of CQDs but not the formation mechanism of CQDs, which mainly derived from the high complexities during the synthesis of CQDs. Among the various synthetic methods, using small molecules as "building blocks", the development of a "bottom-up" approach has promoted the structural design, modulation of the photoluminescence properties, and control of the interfacial properties of CQDs. On the other hand, many works have demonstrated the "building blocks"-dependent properties of CQDs. In this review, from one of the most important variables, the relationships among intrinsic properties of "building blocks" and photoluminescence properties of CQDs are summarized. The topology, chirality, and free radical process are selected as descriptors for the intrinsic properties of "building blocks". This review focuses on the induction and summary of recent research results from the "bottom-up" process. Moreover, several empirical rules pertaining thereto are also proposed.

Triply Hiding Optical Information via Excitation-Dependent Allochroic Photoluminescence Based on Cellulose Derivates

Optical encryption technologies are widely used in information security, whereas the technology with one single optical secret key can be easily cracked. Here, a triple encryption is reported, which hides patterned information in excitation-dependent allochroic materials with long afterglow, enhancing the security level. The allochroic materials are based on a uniaxial co-assembly structure of cellulose nanocrystals (CNCs) and silica. The assembled CNCs present blue emission with quantum yield of 19.8% under 367 nm UV radiation. The blue emission is maintained in the inverse structure when CNCs are calcinated and converted to carbon dots (CDs). The inverse uniaxial-assembly structure improves the CD emission by 6.7 times. The assembly structure can even improve the phosphorescence of CDs, leading to excellent excitation-dependent allochroic properties. Specifically, the materials maintain a cyan long afterglow luminescence at 480 nm after removing 365 nm UV light, whose lifetime is 0.492 s. Changing the excitation wavelength to 254 nm, a UV emission at 343 nm can be obtained, alongside a blue long afterglow luminescence of 420 nm, whose lifetime is 1.574 s. Combining with blue afterglow materials, optical encryption labels are prepared, which hide different patterned information in three scenarios: natural light, UV light, and afterglow luminescence.

The study of a novel high selectivity pyrenyl-based fluorescence probe with aggregation-induced emission characteristics for Fe3+ detection designed by a structure modulation strategy

The past decades have witnessed the feat of fluorescent probes for Fe³⁺ detection, where eliminating the interference by other metal ions plays a pivotal role in its detection by probes in complex environments. Herein, by taking advantage of the substituent effects, the electron-withdrawing group (EWG) -CF₃ and electron-donating group (EDG) -CH₃ were introduced to 2-(1-pyrenyl)pyridine (pypyr) to prepare two turn-off fluorescence probes, 5-trifluoromethyl-2-(1-pyrenyl)pyridine (pypyr-CF3) and 5-methyl-2-(1-pyrenyl)pyridine (pypyr-CH3). Intriguingly, both probes displayed novel aggregation-induced emission (AIE) characteristics in MeCN/H₂O mixtures and the size and morphology of the aggregated particles were studied via DLS and TEM. By the modulation strategy, pypyr-CF3 can detect Fe³⁺ in the presence of 29 different metal ions without interference. Comparatively, pypyr-CH3 experienced serious interference from other metal ions such as Hg²⁺ and Zr⁴⁺. Besides, pypyr-CF3 not only demonstrated a higher photoluminescence quantum yield (PLQY) of 65.25% and wider pH adaptability but is also capable of Fe³⁺ detection over a wide pH range of 2-11 with a short response time (5 seconds). A plausible quenching mechanism based on the inner filter effect has also been demonstrated. More importantly, the versatile applications of pypyr-CF3, such as the quantitative analysis of Fe³⁺ in actual water samples, anti-forgery ink, fingerprint identification, etc., further corroborate its superb capabilities. This study aims to lend concrete support to the design and selectivity modulation of probes.

Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength

Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network k-ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network k-ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties.

Insight into the Modulation of Carbon-Dot Optical Sensing Attributes through a Reduction Pathway

Oxidized/reduced carbon dots (CDs) with tunable optical features have emerged as a new class of CDs having a common "molecular origin" but different fluorescence (FL) behaviors. In the present work, using "banana peel" as a sole carbon source followed by doping with fluorine (F), boron (B), and nitrogen (N) over CDs, banana peel-derived carbon dots (BP-CDs) were synthesized using a well-known hydrothermal synthesis method. Moreover, as-synthesized BP-CDs were further reduced to "rBP-CDs" by NaBH4. At post reduction, the FL performance (i.e., quantum yield) of rBP-CDs were found to be enhanced compared with the BP-CDs, along with variations in excitation and emission wavelengths. Interestingly, the optical sensing attributes of BP-CDs and rBP-CDs were varied, that is, BP-CDs selectively sense "Co2+ with a limit of detection (LOD) value of 180 nM", whereas rBP-CDs detected Co2+ (with an LOD value of 242 nM) as well as Hg2+ (with an LOD value of 190 nM). To the best of our knowledge, this work presents the very first report on the modulation of CDs' sensing behavior after reduction. The modulation in the sensing behavior with the common carbon precursor and reduction paves a new possibility for exploring CDs for different commercial applications.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Facile Synthesis of Aminated Graphene Quantum Dots for Promising and Selective Detection of Cobalt and Copper Ions in Aqueous Media

Due to the rapid development of industrialization, various environmental problems such as water resource pollution are gradually emerging, among which heavy metal pollution is harmful to both human beings and the environment. As a result, there are many metal ion detection methods, among which fluorescence detection stands out because of its rapid, sensitive, low cost and non-toxic characteristics. In recent years, graphene quantum dots have been widely used and studied due to their excellent properties such as high stability, low toxicity and water solubility, and have a broad prospect in the field of metal ion detection. A novel high fluorescence Cu²⁺, Co²⁺ sensing probe produced by graphene quantum hydrothermal treatment is reported. After heat treatment with hydrazine hydrate, the small-molecule precursor nitronaphthalene synthesized by self-nitrification was transformed from blue fluorescent GQDs to green fluorescent amino-functionalized N-GQDs. Compared with other metal ions, N-GQDs are more sensitive to Cu²⁺ and Co²⁺ on the surface, and N-GQDs have much higher selectivity to Cu²⁺ and Co²⁺ than GQDs. The strategy proposed here is simple and economical in design.

A comprehensive review on multi-colored emissive carbon dots as fluorescent probes for the detection of pharmaceutical drugs in water

Exposure to constituent hazardous chemicals in medical products has become a threat to environmental health across the globe. Excessive medication and the mishandling of pharmaceutical drugs can lead to the increased presence of chemicals in the aquatic environment, causing water pollution. Only a few nanomaterials exist for the detection of these chemicals and they are limited in use due to their adverse toxicity, instability, cost, and low aqueous solubility. In contrast, carbon dots (C-dots), a member of the family of carbon-based nanomaterials, have various beneficial properties including excellent biocompatibility, strong photoluminescence, low photobleaching, tunable fluorescence, and easy surface modification. Herein, we summarize recent advancements in various synthetic strategies for high-quality tunable fluorescent C-dots. The root of fluorescence has been briefly explained via the quantum confinement effect, surface defects, and molecular fluorescence. The surface functional moieties of C-dots have been investigated in depth to recognize the various types of pharmaceutical drugs that are used for the treatment of patients. The modulation of C-dot fluorescence in the course of their interactions with these drugs has been carefully explained. Different types of interaction mechanisms behind the C-dot fluorescence alteration have been discussed. Finally, the challenges and future perspectives of C-dots have been proposed for the vibrant field development of C-dot-based drug sensors.

Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia

Androgenetic alopecia (AGA) is a common form of hair loss, which is mainly caused by oxidative stress induced dysregulation of hair follicles (HF). Herein, a highly efficient manganese thiophosphite (MnPS₃) based superoxide dismutase (SOD) mimic was discovered using machine learning (ML) tools. Remarkably, the IC₅₀ of MnPS₃ is 3.61 μg·mL^-1, up to 12-fold lower than most reported SOD-like nanozymes. Moreover, a MnPS₃ microneedle patch (MnMNP) was constructed to treat AGA that could diffuse into the deep skin where HFs exist and remove excess reactive oxygen species. Compared with the widely used minoxidil, MnMNP exhibits higher ability on hair regeneration, even at a reduced frequency of application. This study not only provides a general guideline for the accelerated discovery of SOD-like nanozymes by ML techniques, but also shows a great potential as a next generation approach for rational design of nanozymes.

Diversity and Tailorability of Photoelectrochemical Properties of Carbon Dots

As a new kind of carbon based functional material, carbon dots (CDs) have sparked much interest in recent years. The tunable structure, composition, and morphology of CDs unlocks opportunities to enable diversity in their photoelectrochemical properties, and thus they show great potential in various applications such as biology, catalysis, sensors, and energy storage. Nevertheless, the related understanding of CDs is insufficient at present due to their inherent complexity of microstructure, which involves the intersection of high polymer, bulk carbon, and quantum dot (QD). A good understanding of the underlying mechanism behind the properties of CDs is still a formidable challenge, requiring the integration of robust knowledge from organic chemistry, materials science, and solid state physics. Within this context, discovering more appealing properties, elucidating fundamental factors that affect the properties and proposing effective engineering strategies that can realize specific functions for CDs are now highly pursued by researchers.At the beginning of this Account, the main features of CDs are introduced, where not only the basic structural, compositional and morphological characteristics but also the rich photoelectrochemical properties are elucidated, among which the band gap, chirality, photoinduced potential, and electron sink effect are particularly emphasized. Furthermore, new analysis techniques including transient photoinduced current (TPC), transient photoinduced voltage (TPV), and machine learning (ML) to reveal the unique properties of CDs are described. Then, several appealing strategies that aim to rationally tailor CDs for oriented applications are highlighted. These regulation strategies are morphology modulation (e.g., developing CDs with new geometrical configuration, controlling the particle size), phase engineering (e.g., altering the phase crystallinity, introducing the foreign atoms), surface functionalization (e.g., grafting various types of functional groups), and interfacial tuning (e.g., building CD-based nanohybrids with well-defined interfaces). Although the fundamental investigation of CDs is relatively undeveloped because of their complexity, this does not hinder their wide application. At the same time, exploring the extensive applications of CDs will promote their in-depth understanding. Finally, the chances for building a CD-centered blueprint for sustainable society are explored and challenges for future research in the field of CDs are proposed as follows: (i) the controllable synthesis of CDs with uniform size; (ii) search for novel CDs with unique structure, morphology, or composition; (iii) quantitative understanding of the property of CDs; (iv) performance enhancement by external forces such as magnetism or heat injection; (v) construction of the dual carbon concept; (vi) further research on different photocatalytic applications. On the whole, this Account may provide meaningful references for the understanding of the microstructure-property correlation as well as the regulation of CDs, thereby promoting their transition from fundamental research to practical application.