Stimuli-Responsive Surface Ligands for Direct Lithography of Functional Inorganic Nanomaterials.

ConspectusColloidal nanocrystals (NCs) have emerged as a diverse class of materials with tunable composition, size, shape, and surface chemistry. From their facile syntheses to unique optoelectronic properties, these solution-processed nanomaterials are a promising alternative to materials grown as bulk crystals or by vapor-phase methods. However, the integration of colloidal nanomaterials in real-world devices is held back by challenges in making patterned NC films with the resolution, throughput, and cost demanded by device components and applications. Therefore, suitable approaches to pattern NCs need to be established to aid the transition from individual proof-of-concept NC devices to integrated and multiplexed technological systems.In this Account, we discuss the development of stimuli-sensitive surface ligands that enable NCs to be patterned directly with good pattern fidelity while retaining desirable properties. We focus on rationally selected ligands that enable changes in the NC dispersibility by responding to light, electron beam, and/or heat. First, we summarize the fundamental forces between colloidal NCs and discuss the principles behind NC stabilization/destabilization. These principles are applied to understanding the mechanisms of the NC dispersibility change upon stimuli-induced ligand modifications. Six ligand-based patterning mechanisms are introduced: ligand cross-linking, ligand decomposition, ligand desorption, in situ ligand exchange, ion/ligand binding, and ligand-aided increase of ionic strength. We discuss examples of stimuli-sensitive ligands that fall under each mechanism, including their chemical transformations, and address how these ligands are used to pattern either sterically or electrostatically stabilized colloidal NCs. Following that, we explain the rationale behind the exploration of different types of stimuli, as well as the advantages and disadvantages of each stimulus.We then discuss relevant figures-of-merit that should be considered when choosing a particular ligand chemistry or stimulus for patterning NCs. These figures-of-merit pertain to either the pattern quality (e.g., resolution, edge and surface roughness, layer thickness), or to the NC material quality (e.g., photo/electro-luminescence, electrical conductivity, inorganic fraction). We outline the importance of these properties and provide insights on optimizing them. Both the pattern quality and NC quality impact the performance of patterned NC devices such as field-effect transistors, light-emitting diodes, color-conversion pixels, photodetectors, and diffractive optical elements. We also give examples of proof-of-concept patterned NC devices and evaluate their performance. Finally, we provide an outlook on further expanding the chemistry of stimuli-sensitive ligands, improving the NC pattern quality, progress toward 3D printing, and other potential research directions. Ultimately, we hope that the development of a patterning toolbox for NCs will expedite their implementation in a broad range of applications.

A Low-Measurement-Cost-Based Multi-Strategy Hyperspectral Image Classification Scheme.

The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model's dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU).

Hybrid CNN-Transformer Network for Electricity Theft Detection in Smart Grids.

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.

Direct Heat-Induced Patterning of Inorganic Nanomaterials.

Patterning functional inorganic nanomaterials is an important process for advanced manufacturing of quantum dot (QD) electronic and optoelectronic devices. This is typically achieved by inkjet printing, microcontact printing, and photo- and e-beam lithography. Here, we investigate a different patterning approach that utilizes local heating, which can be generated by various sources, such as UV-, visible-, and IR-illumination, or by proximity heat transfer. This direct thermal lithography method, termed here heat-induced patterning of inorganic nanomaterials (HIPIN), uses colloidal nanomaterials with thermally unstable surface ligands. We designed several families of such ligands and investigated their chemical and physical transformations responsible for heat-induced changes of nanocrystal solubility. Compared to traditional photolithography using photochemical surface reactions, HIPIN extends the scope of direct optical lithography toward longer wavelengths of visible (532 nm) and infrared (10.6 µm) radiation, which is necessary for patterning optically thick layers (e.g., 1.2 µm) of light-absorbing nanomaterials. HIPIN enables patterning of features defined by the diffraction-limited beam size. Our approach can be used for direct patterning of metal, semiconductor, and dielectric nanomaterials. Patterned semiconductor QDs retain the majority of their as-synthesized photoluminescence quantum yield. This work demonstrates the generality of thermal patterning of nanomaterials and provides a new path for additive device manufacturing using diverse colloidal nanoscale building blocks.

Direct Optical Lithography of Colloidal Metal Oxide Nanomaterials for Diffractive Optical Elements with 2π Phase Control.

Spatially patterned dielectric materials are ubiquitous in electronic, photonic, and optoelectronic devices. These patterns are typically made by subtractive or additive approaches utilizing vapor-phase reagents. On the other hand, recent advances in solution-phase synthesis of oxide nanomaterials have unlocked a materials library with greater compositional, microstructural, and interfacial tunability. However, methods to pattern and integrate these nanomaterials in real-world devices are less established. In this work, we directly optically pattern oxide nanoparticles (NPs) by mixing them with photosensitive diazo-2-naphthol-4-sulfonic acid and irradiating with widely available 405 nm light. We demonstrate the direct optical lithography of ZrO2, TiO2, HfO2, and ITO NPs and investigate the chemical and physical changes responsible for this photoinduced decrease in solubility. Micron-thick layers of amorphous ZrO2 NPs were patterned with micron resolution and shown to allow 2π phase control of visible light. We also show multilayer patterning and use it to fabricate features with different thicknesses and distinct structural colors. Upon annealing at 400 °C, the deposited ZrO2 structures have excellent optical transparency across a wide wavelength range (0.3-10 µm), a high refractive index (n = 1.84 at 633 nm), and are optically smooth. We then fabricate diffractive optical elements, such as binary phase diffraction gratings, that show efficient diffractive behavior and good thermal stability. Different oxide NPs can also be mixed prior to patterning, providing a high level of material tunability. This work demonstrates a general patterning approach that harnesses the processability and diversity of colloidal oxide nanomaterials for use in photonic applications.

Cold and hot tumors: from molecular mechanisms to targeted therapy.

Immunotherapy has made significant strides in cancer treatment, particularly through immune checkpoint blockade (ICB), which has shown notable clinical benefits across various tumor types. Despite the transformative impact of ICB treatment in cancer therapy, only a minority of patients exhibit a positive response to it. In patients with solid tumors, those who respond well to ICB treatment typically demonstrate an active immune profile referred to as the "hot" (immune-inflamed) phenotype. On the other hand, non-responsive patients may exhibit a distinct "cold" (immune-desert) phenotype, differing from the features of "hot" tumors. Additionally, there is a more nuanced "excluded" immune phenotype, positioned between the "cold" and "hot" categories, known as the immune "excluded" type. Effective differentiation between "cold" and "hot" tumors, and understanding tumor intrinsic factors, immune characteristics, TME, and external factors are critical for predicting tumor response and treatment results. It is widely accepted that ICB therapy exerts a more profound effect on "hot" tumors, with limited efficacy against "cold" or "altered" tumors, necessitating combinations with other therapeutic modalities to enhance immune cell infiltration into tumor tissue and convert "cold" or "altered" tumors into "hot" ones. Therefore, aligning with the traits of "cold" and "hot" tumors, this review systematically delineates the respective immune characteristics, influencing factors, and extensively discusses varied treatment approaches and drug targets based on "cold" and "hot" tumors to assess clinical efficacy.

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species.

Introduction: This study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade. Methods: We integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383-982 nm), NIR (982-2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance. Results: With optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power. Discussion: These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

Non-volatile rippled-assisted optoelectronic array for all-day motion detection and recognition.

In-sensor processing has the potential to reduce the energy consumption and hardware complexity of motion detection and recognition. However, the state-of-the-art all-in-one array integration technologies with simultaneous broadband spectrum image capture (sensory), image memory (storage) and image processing (computation) functions are still insufficient. Here, macroscale (2 × 2 mm2) integration of a rippled-assisted optoelectronic array (18 × 18 pixels) for all-day motion detection and recognition. The rippled-assisted optoelectronic array exhibits remarkable uniformity in the memory window, optically stimulated non-volatile positive and negative photoconductance. Importantly, the array achieves an extensive optical storage dynamic range exceeding 106, and exceptionally high room-temperature mobility up to 406.7 cm2 V-1 s-1, four times higher than the International Roadmap for Device and Systems 2028 target. Additionally, the spectral range of each rippled-assisted optoelectronic processor covers visible to near-infrared (405 nm-940 nm), achieving function of motion detection and recognition.

Reductive pathways in molten inorganic salts enable colloidal synthesis of III-V semiconductor nanocrystals.

Colloidal quantum dots, with their size-tunable optoelectronic properties and scalable synthesis, enable applications in which inexpensive high-performance semiconductors are needed. Synthesis science breakthroughs have been key to the realization of quantum dot technologies, but important group III-group V semiconductors, including colloidal gallium arsenide (GaAs), still cannot be synthesized with existing approaches. The high-temperature molten salt colloidal synthesis introduced in this work enables the preparation of previously intractable colloidal materials. We directly nucleated and grew colloidal quantum dots in molten inorganic salts by harnessing molten salt redox chemistry and using surfactant additives for nanocrystal shape control. Synthesis temperatures above 425°C are critical for realizing photoluminescent GaAs quantum dots, which emphasizes the importance of high temperatures enabled by molten salt solvents. We generalize the methodology and demonstrate nearly a dozen III-V solid-solution nanocrystal compositions that have not been previously reported.

Memristive switching in the surface of a charge-density-wave topological semimetal.

Owing to the outstanding properties provided by nontrivial band topology, topological phases of matter are considered as a promising platform towards low-dissipation electronics, efficient spin-charge conversion, and topological quantum computation. Achieving ferroelectricity in topological materials enables the non-volatile control of the quantum states, which could greatly facilitate topological electronic research. However, ferroelectricity is generally incompatible with systems featuring metallicity due to the screening effect of free carriers. In this study, we report the observation of memristive switching based on the ferroelectric surface state of a topological semimetal (TaSe4)2I. We find that the surface state of (TaSe4)2I presents out-of-plane ferroelectric polarization due to surface reconstruction. With the combination of ferroelectric surface and charge-density-wave-gapped bulk states, an electric-switchable barrier height can be achieved in (TaSe4)2I-metal contact. By employing a multi-terminal-grounding design, we manage to construct a prototype ferroelectric memristor based on (TaSe4)2I with on/off ratio up to 103, endurance over 103 cycles, and good retention characteristics. The origin of the ferroelectric surface state is further investigated by first-principles calculations, which reveal an interplay between ferroelectricity and band topology. The emergence of ferroelectricity in (TaSe4)2I not only demonstrates it as a rare but essential case of ferroelectric topological materials, but also opens new routes towards the implementation of topological materials in functional electronic devices.

An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure.

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.

Identification of Guiboutia species by NIR-HSI spectroscopy.

Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate, G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.

Ligand-Free Direct Optical Lithography of Bare Colloidal Nanocrystals via Photo-Oxidation of Surface Ions with Porosity Control.

Microscale patterning of colloidal nanocrystal (NC) films is important for their integration in devices. Here, we introduce the direct optical patterning of all-inorganic NCs without the use of additional photosensitive ligands or additives. We determined that photoexposure of ligand-stripped, "bare" NCs in air significantly reduces their solubility in polar solvents due to photo-oxidation of surface ions. Doses as low as 20 mJ/cm2 could be used; the only obvious criterion for material selection is that the NCs need to have significant absorption at the irradiation wavelength. However, transparent NCs can still be patterned by mixing them with suitably absorbing NCs. This approach enabled the patterning of bare ZnSe, CdSe, ZnS, InP, CeO2, CdSe/CdS, and CdSe/ZnS NCs as well as mixtures of ZrO2 or HfO2 NCs with ZnSe NCs. Optical, X-ray photoelectron, and infrared spectroscopies show that solubility loss results from desorption of bound solvent due to photo-oxidation of surface ions. We also demonstrate two approaches, compatible with our patterning method, for modulating the porosity and refractive index of NC films. Block copolymer templating decreases the film density, and thus the refractive index, by introducing mesoporosity. Alternatively, hot isostatic pressing increases the packing density and refractive index of NC layers. For example, the packing fraction of a ZnS NC film can be increased from 0.51 to 0.87 upon hot isostatic pressing at 450 °C and 15 000 psi. Our findings demonstrate that direct lithography by photo-oxidation of bare NC surfaces is an accessible patterning method for facilitating the exploration of more complex NC device architectures while eliminating the influence of bulky or insulating surfactants.

The mechanism of digitized landscape architecture design under edge computing.

The purposes are to analyze the mechanism of digitized landscape architecture design and stablize the garden landscape image display in constructing garden landscape digitization platform. According to previous research and mobile edge computing, a scheme of digitized landscape architecture design is proposed based on edge computing. This scheme uses discrete elevation calculation to preserve the landscape design image's frame. It adopts the Roberts edge detection and Laplacian operator for high-level stable preservation of landscape images. Simultaneously, the displayed image is stablized using edge computing algorithms. Simulation experiments are performed to verify the effectiveness of the proposed scheme of digitized landscape architecture design based on mobile edge computing. Results demonstrate that the discrete elevation calculation algorithm can avoid low visual rendering in the 3D image generation process, optimize the seed point matching of edge correlation, and ensure image clarity and stability. The edge computing algorithm can fundamentally avoid the problem of image shaking. The impact of different algorithm models on the classification and accuracy of landscape images is analyzed through parameter optimization. Compared with some latest models, the proposed landscape design scheme based on edge computing has better accuracy. The average accuracy can reach more than 90%, and the Kappa coefficient remains at 86.93%. The designed garden landscape digitization platform can stably display 3D garden landscape images while avoiding the shaking of 3D images, which can provide a theoretical basis and practical value for designing and planning landscape architecture.

Direct Optical Patterning of Quantum Dot Light-Emitting Diodes via In Situ Ligand Exchange.

Precise patterning of quantum dot (QD) layers is an important prerequisite for fabricating QD light-emitting diode (QLED) displays and other optoelectronic devices. However, conventional patterning methods cannot simultaneously meet the stringent requirements of resolution, throughput, and uniformity of the pattern profile while maintaining a high photoluminescence quantum yield (PLQY) of the patterned QD layers. Here, a specially designed nanocrystal ink is introduced, "photopatternable emissive nanocrystals" (PENs), which satisfies these requirements. Photoacid generators in the PEN inks allow photoresist-free, high-resolution optical patterning of QDs through photochemical reactions and in situ ligand exchange in QD films. Various fluorescence and electroluminescence patterns with a feature size down to ≈1.5 µm are demonstrated using red, green, and blue PEN inks. The patterned QD films maintain ≈75% of original PLQY and the electroluminescence characteristics of the patterned QLEDs are comparable to thopse of non-patterned control devices. The patterning mechanism is elucidated by in-depth investigation of the photochemical transformations of the photoacid generators and changes in the optical properties of the QDs at each patterning step. This advanced patterning method provides a new way for additive manufacturing of integrated optoelectronic devices using colloidal QDs.

Direct Wavelength-Selective Optical and Electron-Beam Lithography of Functional Inorganic Nanomaterials.

Direct optical lithography of functional inorganic nanomaterials (DOLFIN) is a photoresist-free method for high-resolution patterning of inorganic nanocrystals (NCs) that has been demonstrated using deep UV (DUV, 254 nm) photons. Here, we expand the versatility of DOLFIN by designing a series of photochemically active NC surface ligands for direct patterning using various photon energies including DUV, near-UV (i-line, 365 nm), blue (h-line, 405 nm), and visible (450 nm) light. We show that the exposure dose for DOLFIN can be ∼30 mJ/cm2, which is small compared to most commercial photopolymer resists. Patterned nanomaterials can serve as highly robust optical diffraction gratings. We also introduce a general approach for resist-free direct electron-beam lithography of functional inorganic nanomaterials (DELFIN) which enables all-inorganic NC patterns with feature size down to 30 nm, while preserving the optical and electronic properties of patterned NCs. The designed ligand chemistries and patterning techniques offer a versatile platform for nano- and micron-scale additive manufacturing, complementing the existing toolbox for device fabrication.

Specific and Sensitive Isothermal Electrochemical Biosensor for Plant Pathogen DNA Detection with Colloidal Gold Nanoparticles as Probes.

Developing quick and sensitive molecular diagnostics for plant pathogen detection is challenging. Herein, a nanoparticle based electrochemical biosensor was developed for rapid and sensitive detection of plant pathogen DNA on disposable screen-printed carbon electrodes. This 60 min assay relied on the rapid isothermal amplification of target pathogen DNA sequences by recombinase polymerase amplification (RPA) followed by gold nanoparticle-based electrochemical assessment with differential pulse voltammetry (DPV). Our method was 10,000 times more sensitive than conventional polymerase chain reaction (PCR)/gel electrophoresis and could readily identify P. syringae infected plant samples even before the disease symptoms were visible. On the basis of the speed, sensitivity, simplicity and portability of the approach, we believe the method has potential as a rapid disease management solution for applications in agriculture diagnostics.