Developing efficient and sustainable photocatalysts for CO2 reduction remains a significant challenge, particularly with environmentally benign materials. Here, we report the first one-step synthesis of metal-lead-free perovskite heterostructural nanocrystals by decorating Cs3Sb2Cl9 perovskite nanorods with size-controlled Pd nanoclusters via a one-step hot-injection method. The resulting Pd-Cs3Sb2Cl9 heteronanorods (HNRs) exhibit strong interfacial electronic coupling, enhanced charge separation, and excellent colloidal stability. Transient absorption spectroscopy and DFT calculations reveal a built-in electric field that drives directional electron transfer from the perovskite host to the Pd domains. Under UV irradiation, the Pd-Cs3Sb2Cl9 HNRs demonstrate excellent CO2 photoreduction activity with high CH4 selectivity, achieving a record apparent quantum yield (AQY) of 2.62% among halide perovskite nanocrystal-based systems with a large electronic yield of 689.3 +/- 12.2 mu molgcat -1. In situ spectroscopic monitoring and Gibbs free energy analysis further unveil a Pd-facilitated reaction pathway involving stabilization of key intermediates. This work introduces a new class of lead-free perovskite-based heterostructures through a facile one-step synthesis strategy and offers a new design principle for next-generation photocatalysts for solar fuel production.

Detecting molecules across different spatial scales remains a major challenge in biological samples, particularly in complex tissues such as brain, where linking molecular identification and nanoscale structural detail is essential for understanding function. Fluorescence microscopy provides selective labeling of specific molecules but cannot fully resolve many cellular and subcellular features, whereas electron microscopy reveals ultrastructure without molecular specificity. Bridging these modalities is difficult because commonly used fluorescent probes such as dyes and proteins cannot provide electron contrast. Quantum dots are particularly useful as they retain fluorescence while providing sufficient electron density for ultrastructural visualization. Here, we show that streptavidin-functionalized CdSe/CdS core/shell quantum dots can function as dual-modal probes for labeling γ-aminobutyric acid (GABA) neurons in brain tissue. The quantum dots produce bright and stable fluorescence signals for optical detection while their inorganic composition generates strong electron contrast in ultrathin sections examined by transmission electron microscopy. This work demonstrates that functionalized CdSe/CdS quantum dots can serve as stable dual-modal probes for fluorescence and electron microscopy in complex tissues, highlighting the potential of semiconductor nanocrystals for multifunctional imaging applications.

ABSTRACT Developing efficient and sustainable photocatalysts for CO 2 reduction remains a significant challenge, particularly with environmentally benign materials. Here, we report the first one‐step synthesis of metal–lead‐free perovskite heterostructural nanocrystals by decorating Cs 3 Sb 2 Cl 9 perovskite nanorods with size‐controlled Pd nanoclusters via a one‐step hot‐injection method. The resulting Pd‐Cs 3 Sb 2 Cl 9 heteronanorods (HNRs) exhibit strong interfacial electronic coupling, enhanced charge separation, and excellent colloidal stability. Transient absorption spectroscopy and DFT calculations reveal a built‐in electric field that drives directional electron transfer from the perovskite host to the Pd domains. Under UV irradiation, the Pd‐Cs 3 Sb 2 Cl 9 HNRs demonstrate excellent CO 2 photoreduction activity with high CH 4 selectivity, achieving a record apparent quantum yield (AQY) of 2.62% among halide perovskite nanocrystal‐based systems with a large electronic yield of 689.3 ± 12.2 µmol·g cat −1 . In situ spectroscopic monitoring and Gibbs free energy analysis further unveil a Pd‐facilitated reaction pathway involving stabilization of key intermediates. This work introduces a new class of lead‐free perovskite‐based heterostructures through a facile one‐step synthesis strategy and offers a new design principle for next‐generation photocatalysts for solar fuel production.

Perovskite quantum dots (PQDs) exhibit pronounced photoluminescence (PL) intensity and lifetime fluctuations at the single-particle level that arise from competition between radiative and trap-mediated nonradiative exciton recombination pathways. Although surface treatments are widely employed to mitigate these fluctuations, the mechanistic relationship among surface chemistry, trap state energetics, and exciton dynamics remains poorly understood. Here, we present an integrative and comparative study that correlates PL intensity fluctuation with fluorescence lifetime-intensity distribution (FLID) patterns in single CsPbBr3 QDs. This work analyzes PQDs capped with conventional ligands, a zwitterionic ligand, or treated postsynthetically with excess bromide. Three distinct PL intensity fluctuation behaviors, blinking, flickering, and minimal fluctuations, were observed, with relative populations that strongly depend on surface passivation. FLID analysis reveals that these behaviors originate from nonradiative recombination pathways involving trap states of various energies. By correlating the observed FLID patterns with density functional theory calculations, we demonstrate how ligand binding strength and binding mode influence the accessibility of specific surface defect states. Strong, multidentate binding of the zwitterionic ligand enhances surface stability and suppresses both blinking and flickering by limiting dynamic ligand desorption and trap formation. In contrast, excess bromide treatment selectively alters shallow trap-mediated pathways, modifying PL fluctuation characteristics. These results provide an atomistic framework for understanding how surface chemistry and trap states evolve under different passivation methods and offer insight into strategies for improving the stability and optical performance of PQDs.

Gold nanoparticles (AuNPs) exhibit strong light absorption and scattering properties due to localized surface plasmon resonance, making them valuable tools in optical sensing and imaging applications. Direct visual recognition of single AuNPs enables simple and ultrasensitive detection. In this study, we report an approach for the detection and quantification of AuNPs using dark-field scattering light microscopy images captured with a mobile phone camera. Deep learning was incorporated for image analysis to promote ultrasensitive recognition and detection of 120 nm AuNPs with concentrations ranging from 5.3 to 530 fM. Preprocessed images were split into training and testing data to build two deep-learning models, i.e., classification and regression. The classification model achieved perfect precision, recall, and F1 score with a two-image input strategy, while the regression model demonstrated a correlation coefficient of 0.9999 between predicted and actual concentrations. Blind tests of 4 samples at different concentrations confirmed the method's prediction accuracy, with recovery rates of 97-108%. This work presents a simple, easily accessible, and highly sensitive platform for AuNPs detection with potential applications in a wide range of sensing tasks, leveraging the accessibility of mobile phone cameras and the robustness of deep learning techniques.

The practical implementation of mechanoluminescent (ML) materials in applications such as pressure sensing, energy harvesting, and human-machine interaction is contingent upon achieving higher emission efficiencies. This work reports the design of an inorganic-organic composite material with high ML efficiency achieved by co-doping Mn2+ and Fe3+ in ZnS microparticles and incorporating them into a porous poly(vinylidene fluoride-co-hexafluoropropylene) matrix. The co-doping of Mn2+ and Fe3+ effectively modifies the bandgap structure of ZnS, enabling an energy transfer process that enhances not only mechanoluminescence but also photoluminescence and afterglow emission. Additionally, the polymer substrate, benefiting from its porous structure, generates a local piezoelectric field that further amplifies the ML emission of the doped ZnS microparticles. The resulting composite membrane, characterized by significantly enhanced emission intensity and tunable, repeatable responses to external force, is anticipated to unlock new possibilities for ML materials in smart sensing and advanced display applications.

Perovskite nanocrystals (PNCs) show great promise for optoelectronic devices; yet at the single-particle level, they are susceptible to photoluminescence (PL) fluctuations. Single-particle studies provide key insights into the photophysical processes responsible for these fluctuations. This review discusses both intrinsic factors, such as size and surface defects, and extrinsic factors, including moisture and oxygen, that contribute to PL instability in PNCs. We also highlight recent advancements in surface passivation techniques that effectively reduce or suppress the PL fluctuations, thereby enhancing the stability and optical performance of PNCs. Ultimately, understanding and mitigating PL fluctuations are essential for improving the stability and efficiency of PNC-based devices.

Semiconductor nano-crystals, known as quantum dots (QDs), have attracted significant attention for their unique fluorescence properties. Under continuous excitation, QDs emit photons with intricate intensity fluctuation: the intensity of photon emission fluctuates during the excitation, and such a fluctuation pattern can vary across different QDs even under the same experimental conditions. What adding to the complication is that the processed intensity series are non-Gaussian and truncated due to necessary thresholding and normalization. Conventional normality-based single-dot analysis fall short of addressing these complexities. In collaboration with chemists, we develop an integrative learning approach to simultaneously analyzing intensity series from multiple QDs. Motivated by the unique data structure and the hypothesized behaviors of the QDs, our approach leverages the celebrated hidden Markov model as its structural backbone to characterize individual dot intensity fluctuations, while assuming that, in each state the normalized intensity follows a 0/1 inflated Beta distribution, the state/emission distributions are shared across the QDs, and the state transition dynamics can vary among a few QD clusters. This framework allows for a precise, collective characterization of intensity fluctuation patterns and have the potential to transform current practice in chemistry. Applying our method to experimental data from 128 QDs, we reveal three shared intensity states and capture several distinct intensity transition patterns, underscoring the effectiveness of our approach in providing deeper insights into QD behaviors and their design and application potential.

Fluorescence fluctuations were firstly observed in single colloidal quantum dots (QDs) over three decades ago. The practice of analyzing fluorescence fluctuations in single QDs typically involves setting a subjective threshold which could lead to bias in determining the QD fluorescence behavior. In our studies, varying statistical tools were applied to analyze single QDs of different compositions to achieve a more objective and robust assessment of their properties. By employing these approaches, we identified fast fluorescence lifetime blinking in single CdSe/CdS core/shell QDs. We developed an integrative statistical analysis of single CsPbBr3 perovskite and categorized them into “blinking” and “flickering” based on their unique fluorescence intermittency behavior. These discoveries allow for better understanding of the underlying mechanism causing fluorescence intermittency in colloidal QDs.

A persistent challenge in utilizing gold nanostructures for surface-enhanced Raman scattering (SERS) lies in positioning analytes into the nanoscale hotspots that maximize SERS performance and achieve universal substrates. Here, a novel mesoporous nano-pockets strategy is proposed to design high performance universal SERS substrates by precisely capturing target molecules into plasmonic hotspots. This is achieved through utilizing mesoporous silica as nano-pockets for confined growth of short Au thorns on the Fe3O4 core (Fe3O4@sAT@mSiO2), whilst the vertically open nano-pockets structure with nanospace large enough at tips of short Au thorns to capture target molecules through hydroxyl group on the mesoporous surface. The electromagnetic field enhancement obtained from the Au thorns with an average gap size of 3 nm is sufficient to amplify the Raman signal peaks of the trapped molecules. Therefore, the as-prepared Fe3O4@sAT@mSiO2 proves to be ultrasensitive and reliable SERS detection of 15 kinds of drugs, with all limits of detection lower than those reported in the current literature. Moreover, the experimental findings are further corroborated by simulations using the molecular dynamics and finite element method. These mesoporous-confined Au thorn nano-pockets with accessible hotspots promote future use for developing the universal SERS method in various fields.

Semiconductor nano-crystals, known as quantum dots (QDs), have garnered significant interest in various scientific fields due to their unique fluorescence properties. One captivating characteristic of QDs is their ability to emit photons under continuous excitation. The intensity of photon emission fluctuates during the excitation, and such a fluctuation pattern can vary across different dots even under the same experimental conditions. What adding to the complication is that the processed intensity series are non-Gaussian and truncated due to necessary thresholding and normalization. As such, conventional approaches in the chemistry literature, typified by single-dot analysis of raw intensity data with Gaussian hidden Markov models (HMM), cannot meet the many analytical challenges and may fail to capture any novel yet rare fluctuation patterns among QDs. Collaborating with scientists in the chemistry field, we have developed an integrative learning approach to simultaneously analyzing intensity series of multiple QDs. Our approach still inherits the HMM as the skeleton to model the intensity fluctuations of each dot, and based on the data structure and the hypothesized collective behaviors of the QDs, our approach asserts that (i) under each hidden state, the normalized intensity follows a 0/1 inflated Beta distribution, (ii) the state distributions are shared across all the QDs, and (iii) the patterns of transitions can vary across QDs. These unique features allow for a precise characterization of the intensity fluctuation patterns and facilitate the clustering of the QDs. With experimental data collected on 128 QDs, our methods reveal several QD clusters characterized by unique transition patterns across three intensity states. The results provide deeper insight into QD behaviors and their design/application potentials.

Chemical sensor arrays combined with machine learning are promising for applications requiring odor detection, classification, and quantification. This study investigates the scaling of nanoparticle-based integrated chemical sensors using pico-drop technology to deposit nanoparticle solutions with micron-scale alignment precision. Gold nanoparticles using 12 different capping ligands were deposited onto lithographically patterned electrodes, investigating mass per device, droplet volume, spot placement, and solvent evaporation rates with a Scienion S3 SciFLEXARRAYER. Response sensitivity increased with more mass deposited. Larger drop volumes also increased sensitivity. Conversely, distributing the same nanoparticle mass across multiple spots reduced responses. Sensor responses increased with solvent evaporation temperature from 15 to 50[Formula: see text]C, exhibiting the highest and most consistent responses at 50[Formula: see text]C. Device yield varied with ligand chemistry, averaging 90% across an integrated chip with 140 sensor elements and 12 chemistries. Optimally fabricated sensor arrays were tested for VOC detection in room-air and compared to measurements in nitrogen and dry air. 100% classification accuracy was achieved for sensing acetone, ethanol, hexane, propanol, and toluene in nitrogen as well as room air using random forest and boosted trees. Results demonstrate that scaling sensor arrays improves sensitivity and reduces variability for enhanced vapor detection, classification, and quantification.

Hole scavengers are often employed in photocatalytic reactions catalyzed by semiconductors to efficiently extract photogenerated holes, thereby suppressing charge recombination and enhancing the overall catalytic activity. Beyond improving charge separation, the type of hole scavengers can also affect the activity, selectivity, and mechanism of the reaction. Interestingly, our findings in this work reveal that not only the identity but also the amount of hole scavenger plays a significant role, indicating the reaction pathway. Specifically, in nitrobenzene reduction catalyzed by CdS quantum dots (QDs), the concentration of hole scavenger Na2SO3 influences the reaction pathway and the final products: low concentrations (2-8 mM) favored the direct reduction pathway and yielded phenylhydroxylamine and aniline, while high concentrations (12-24 mM) favored an indirect (coupling) pathway and produced azoxybenzene. We hypothesized that SO4˙- radicals formed at high concentrations of Na2SO3 in the presence of dissolved oxygen is responsible for this change in reduction pathway. The existence of SO4˙- radicals was validated by Electron Spin Resonance spectroscopy. Quenching of SO4˙- radicals using tert-butyl alcohol (TBA) reverted the reaction back to the direct reduction pathway even when a high concentration of Na2SO3 was added, confirming the critical role of SO4˙-. Density functional theory calculations revealed that the SO4˙- adsorbed onto the CdS surface abstracts hydrogen from the reaction intermediates, promoting the indirect coupling reaction. In addition, photoluminescence and femtosecond transient absorption studies showed that rapid hole trapping in CdS QDs occurred within 3-4 picoseconds after excitation, and Na2SO3 scavenged trapped holes at a timescale of tens of nanoseconds. These findings highlight the diverse functions of hole scavengers in photocatalysis. Their quantity and the species generated after hole scavenging can direct the reaction pathways and product selectivity.

Immunogold, that is, gold nanoparticles (AuNPs) conjugated with biomolecules such as antibodies and peptides, have been widely used to construct sandwiched immunosensors for biodetection. Two main challenges in these immunoassays are difficulties in finding and validating a suitable antibody, and the nonspecific interaction between the substrate and immunogold, which lowers the detection sensitivity and even causes false results. To avoid these issues, we took advantage of the nonspecific interaction between AuNPs and capture antibodies and proposed a new sensing mechanism. That is, after the capture of analyte targets by the capture antibodies on the substrate, AuNPs of certain chemical functionality would preferably bind to the free capture antibodies. Consequently, the amount of deposited AuNPs will inversely depend on the concentration of the analytes. As a proof-of-concept, we designed a mass-based sensor where anti-IgG antibodies were coated on a quartz crystal microbalance substrate. After IgG was introduced, tannic acid-capped AuNPs were applied to bind with the free anti-IgG antibody molecules. A frequency change (Δf) of the quartz substrate was induced by the increased mass loading. To further amplify the loading mass, an Ag enhancer solution was added, and Ag growth was catalyzed by the bound AuNPs. The Δf response showed a concentration-dependent decrease when increasing IgG concentration with a detection limit of 2.6 ng/mL. This method relies on the nonspecific interaction between AuNPs and anti-IgG antibodies to realize sensitive detection of IgG and eliminates the use of detection antibodies. The concept is an alternative to many existing immunoassay technologies.

Colloidal quantum-confined perovskite CsPbBr3 nanoplatelets (NPLs) exhibit blue emission, making them promising for blue LEDs. It is known that the thickness of CsPbBr3 NPLs is not stable especially in polar solvents and during long-term storage, causing their absorption and emission to red-shift due to the transformation to a larger structure. To cope with this limitation, we developed a surface coating method to protect the NPLs. In particular, we designed a system to stabilize the thickness of NPLs during the silica coating process. We also explored the impact of functional groups of the capping ligands and reaction time on the spectroscopic performance of the NPLs to gain some insights into the surface passivation mechanism, which will benefit future shell growth methods using other materials, not only limited to silica. Under the optimal reaction conditions, a photoluminescence quantum yield of 81% was achieved for the silica coated CsPbBr3 NPLs. These NPLs exhibited good stability in water and other polar solvents and after long-term storage. Density functional theory calculations show that the binding affinity of water on the NPL is decreased by silica coating. Based on the findings in this study, we proposed a mechanism of how a coating formed on the NPL surface to improve the stability and enhance the performance of CsPbBr3 NPLs.

3D surface-enhanced Raman scattering (SERS) platforms with multi-dimensional hotspots and better tolerance to laser misfocus show great potential for the rapid detection of trace fentanyl. However, the complex fabrication process of 3D fine structures limits the wide-ranging application of the SERS technique. Herein, a 3D SERS-active platform is fabricated utilizing a scalable and cost-effective strategy that involves the electroless deposition of dense Au nanoparticles on natural butterfly wing scales (b-wings@Au). The resultant b-wings@Au proves to be sensitive and reliable, with a lower limit of detection of 4x 10-12 m and an enhancement factor (EFexp) of 1.74 x 108 for fentanyl. Furthermore, it exhibits excellent reproducibility, as evidenced by a relative standard deviation of less than 5%, and also demonstrates outstanding stability. The obtained high EFexp is due to the combined effects of the electromagnetic mechanism, involving the enhanced electromagnetic field generated by 3D periodic hotspots, and the chemical mechanism, involving the charge transfer from fentanyl to Au surface. Additionally, the as-prepared 3D substrate realizes the trace detection of nine fentanyl analogs. This study provides new ideas for the controllable construction of sensitive SERS platforms for drug detection, ultimately advancing the fields of 3D substrates in booming SERS-based technologies. A sensitive 3D surface-enhanced Raman scattering (SERS) platform is fabricated using a scalable and cost-effective strategy that involves the electroless deposition of dense Au nanoparticles on natural butterfly wing scales, exerting remarkable SERS performance with a lower limit of detection of 4 x 10-12 m and an enhancement factor of 1.74 x 108 for fentanyl. image

Biochar has a limited initial adsorption capacity and number of adsorption sites. Nevertheless, by employing physical or chemical techniques, the multi-level architecture of biochar can be altered to achieve substantial improvements in aquatic pollutant adsorption. This can unleash biochar's full potential for environmental remediation. This review provides a summary of the transformative effects of various modification techniques on the multi-level structure of biochar, as well as their unique adsorption mechanisms in different water conditions. Furthermore, the effectiveness of adapted biochar in its preparation and application will be deliberated, emphasizing its substantial potential in the restoration of water environments. The findings and recommendations presented in this literature review offer significant guidance for improving biochar production and utilization, thus contributing to a cleaner and healthier future.

X-ray free electron laser (XFEL) microcrystallography and synchrotron single-crystal crystallography are used to evaluate the role of organic substituent position on the optoelectronic properties of metal-organic chalcogenolates (MOChas). MOChas are crystalline 1D and 2D semiconducting hybrid materials that have varying optoelectronic properties depending on composition, topology, and structure. While MOChas have attracted much interest, small crystal sizes impede routine crystal structure determination. A series of constitutional isomers where the aryl thiol is functionalized by either methoxy or methyl ester are solved by small molecule serial femtosecond X-ray crystallography (smSFX) and single crystal rotational crystallography. While all the methoxy examples have a low quantum yield (0-1%), the methyl ester in the ortho position yields a high quantum yield of 22%. The proximity of the oxygen atoms to the silver inorganic core correlates to a considerable enhancement of quantum yield. Four crystal structures are solved at a resolution range of 0.8-1.0 & Aring; revealing a collapse of the 2D topology for functional groups in the 2- and 3- positions, resulting in needle-like crystals. Further analysis using density functional theory (DFT) and many-body perturbation theory (MBPT) enables the exploration of complex excitonic phenomena within easily prepared material systems.

The instability of colloidal lead halide perovskite nanocrystals (NCs) presents a significant challenge for their application in optoelectronic devices. This review examines the primary causes of instability in these NCs and the proposed mechanisms of degradation. It also introduces the recently developed synthesis and surface passivation methods to address the instability issue of colloidal perovskite NCs. Specifically, we focus on the various types of ligands and precursors introduced during NC synthesis or post-treatment and how they impact the structural and optical properties of the perovskite NCs. This review also proposes a systematic approach to evaluating stability enhancement strategies by establishing key parameters and ranking them based on working and processing conditions. Finally, we discuss the issues that need to be addressed in future research to achieve practical application of lead halide perovskite NCs in advanced optoelectronic systems.