An ultra-sensitive colloidal quantum dot infrared photodiode exceeding 100 000% external quantum efficiency via photomultiplication.

In this study, we present ultrasensitive infrared photodiodes based on PbS colloidal quantum dots (CQDs) using a double photomultiplication strategy that utilizes the accumulation of both electron and hole carriers. While electron accumulation was induced by ZnO trap states that were created by treatment in a humid atmosphere, hole accumulation was achieved using a long-chain ligand that increased the barrier to hole collection. Interestingly, we obtained the highest responsivity in photo-multiplicative devices with the long ligands, which contradicts the conventional belief that shorter ligands are more effective for optoelectronic devices. Using these two charge accumulation effects, we achieved an ultrasensitive detector with a responsivity above 7.84 × 102 A W-1 and an external quantum efficiency above 105% in the infrared region. We believe that the photomultiplication effect has great potential for surveillance systems, bioimaging, remote sensing, and quantum communication.

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.

Understanding Colloidal Quantum Dot Device Characteristics with a Physical Model.

Colloidal quantum dots (CQDs) are finding increasing applications in optoelectronic devices, such as photodetectors and solar cells, because of their high material quality, unique and attractive properties, and process flexibility without the constraints of lattice match and thermal budget. However, there is no adequate device model for colloidal quantum dot heterojunctions, and the popular Shockley-Quiesser diode model does not capture the underlying physics of CQD junctions. Here, we develop a compact, easy-to-use model for CQD devices rooted in physics. We show how quantum dot properties, QD ligand binding, and the heterointerface between quantum dots and the electron transport layer (ETL) affect device behaviors. We also show that the model can be simplified to a Shockley-like equation with analytical approximate expressions for reverse saturation current, ideality factor, and quantum efficiency. Our model agrees well with the experiment and can be used to describe and optimize CQD device performance.

Sample preconcentration through airjet-induced liquid phase enrichment.

Sample preparation is essential for nucleic acid assays, affecting their sensitivity and reliability. However, this process often results in a significant loss or dilution of the analyte, which becomes a bottleneck that limits downstream assay performance, particularly for assays that accept a limited input sample volume. To overcome this challenge, we present an evaporative-based sample enrichment method that uses an airjet to concentrate analytes within a small, defined volume by reversing the coffee-ring effect. A small, concentrated sample can then be collected for analysis to increase the initial sample load. The effectiveness of the reported airjet enrichment was quantified using qPCR of λ-DNA, HeLa-S3 RNA, and heat-inactivated SARS-CoV-2 samples. Comparisons between airjet enrichment and conventional evaporative concentration methods demonstrated significant advantages of airjet enrichment, including the ability to concentrate a high percentage of analyte within a 1 µL volume. The enrichment method was then integrated and adapted for various fluid volumes commonly found in nucleic acid sample preparation procedures. Here, airjet enrichment reduced the overall Cq by an average of 9.27 cycles for each analyte, resulting in a 600-fold enrichment from the initial concentration. To perform selective enrichment and prevent salt-based interference in downstream analysis, PEG was added to reduce the co-enrichment of salt. In addition, a preliminary study was conducted to explore the integration of airjet enrichment into ELISA using rabbit IgG as a model antigen. These findings demonstrate how airjet enrichment can be easily integrated into existing laboratory protocols with minimal modification and significantly improve the performance of biosensors.

Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing.

Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex and human-vision uninterpretable morphological features within a heterogeneous cell population. However, the limited capabilities and complicated implementation of deep learning-assisted IACS systems reported to date hinder the adoption of the systems for a wide range of biomedical research. Here, we present image-activated cell sorting by applying fast deep learning algorithms to conduct cell sorting without labeling. The overall sorting latency, including signal processing and AI inferencing, is less than 3 ms, and the training time for the deep learning model is less than 30 min with a training dataset of 20,000 images. Both values set the record for IACS with sorting by AI inference. . We demonstrated our system performance through a 2-part polystyrene beads sorting experiment with 96.6% sorting purity, and a 3-part human leukocytes sorting experiment with 89.05% sorting purity for monocytes, 92.00% sorting purity for lymphocytes, and 98.24% sorting purity for granulocytes. The above performance was achieved with simple hardware containing only 1 FPGA, 1 PC and GPU, as a result of an optimized custom CNN UNet and efficient use of computing power. The system provides a compact, sterile, low-cost, label-free, and low-latency cell sorting solution based on real-time AI inferencing and fast training of the deep learning model.

Anti-KIT DNA aptamer-conjugated porous silicon nanoparticles for the targeted detection of gastrointestinal stromal tumors.

Evaluation of Gastrointestinal Stromal Tumors (GIST) during initial clinical staging, surgical intervention, and postoperative management can be challenging. Current imaging modalities (e.g., PET and CT scans) lack sensitivity and specificity. Therefore, advanced clinical imaging modalities that can provide clinically relevant images with high resolution would improve diagnosis. KIT is a tyrosine kinase receptor overexpressed on GIST. Here, the application of a specific DNA aptamer targeting KIT, decorated onto a fluorescently labeled porous silicon nanoparticle (pSiNP), is used for the in vitro & in vivo imaging of GIST. This nanoparticle platform provides high-fidelity GIST imaging with minimal cellular toxicity. An in vitro analysis shows greater than 15-fold specific KIT protein targeting compared to the free KIT aptamer, while in vivo analyses of GIST-burdened mice that had been injected intravenously (IV) with aptamer-conjugated pSiNPs show extensive nanoparticle-to-tumor signal co-localization (>90% co-localization) compared to control particles. This provides an effective platform for which aptamer-conjugated pSiNP constructs can be used for the imaging of KIT-expressing cancers or for the targeted delivery of therapeutics.

Athermalized carrier multiplication mechanism for detectors using an amorphous silicon gain medium.

In this paper, we investigate the temperature sensitivity of gain and breakdown voltage of detectors based on cycling excitation process (CEP), an internal signal amplification mechanism found in amorphous silicon (a-Si). Changes in gain and breakdown voltage with temperature can result in pixel-to-pixel signal variation in a focal plane array and variations in photon detection efficiency for single photon detectors. We have demonstrated athermalized CEP detectors with their gain and breakdown voltage being nearly temperature independent from 200 K to 350 K, covering the temperature range for practical applications. The device appears to be more thermally stable than avalanche photodetectors (APDs) with different gain media such as Si, InP, InAlAs, etc. The excellent thermal stability of CEP detectors is attributed to the field-enhanced tunneling process for excitation of localized carriers into the mobile bands, which dominates over the phonon excitation process.

Perovskite superlattices with efficient carrier dynamics.

Compared with their three-dimensional (3D) counterparts, low-dimensional metal halide perovskites (2D and quasi-2D; B2An-1MnX3n+1, such as B = R-NH3+, A = HC(NH2)2+, Cs+; M = Pb2+, Sn2+; X = Cl-, Br-, I-) with periodic inorganic-organic structures have shown promising stability and hysteresis-free electrical performance1-6. However, their unique multiple-quantum-well structure limits the device efficiencies because of the grain boundaries and randomly oriented quantum wells in polycrystals7. In single crystals, the carrier transport through the thickness direction is hindered by the layered insulating organic spacers8. Furthermore, the strong quantum confinement from the organic spacers limits the generation and transport of free carriers9,10. Also, lead-free metal halide perovskites have been developed but their device performance is limited by their low crystallinity and structural instability11. Here we report a low-dimensional metal halide perovskite BA2MAn-1SnnI3n+1 (BA, butylammonium; MA, methylammonium; n = 1, 3, 5) superlattice by chemical epitaxy. The inorganic slabs are aligned vertical to the substrate and interconnected in a criss-cross 2D network parallel to the substrate, leading to efficient carrier transport in three dimensions. A lattice-mismatched substrate compresses the organic spacers, which weakens the quantum confinement. The performance of a superlattice solar cell has been certified under the quasi-steady state, showing a stable 12.36% photoelectric conversion efficiency. Moreover, an intraband exciton relaxation process may have yielded an unusually high open-circuit voltage (VOC).

Multimodal NASH prognosis using 3D imaging flow cytometry and artificial intelligence to characterize liver cells.

To improve the understanding of the complex biological process underlying the development of non-alcoholic steatohepatitis (NASH), 3D imaging flow cytometry (3D-IFC) with transmission and side-scattered images were used to characterize hepatic stellate cell (HSC) and liver endothelial cell (LEC) morphology at single-cell resolution. In this study, HSC and LEC were obtained from biopsy-proven NASH subjects with early-stage NASH (F2-F3) and healthy controls. Here, we applied single-cell imaging and 3D digital reconstructions of healthy and diseased cells to analyze a spatially resolved set of morphometric cellular and texture parameters that showed regression with disease progression. By developing a customized autoencoder convolutional neural network (CNN) based on label-free cell transmission and side scattering images obtained from a 3D imaging flow cytometer, we demonstrated key regulated cell types involved in the development of NASH and cell classification performance superior to conventional machine learning methods.

High Sensitivity, Rapid Detection of Virus in High Traffic Environments.

The global pandemic caused by the SARS-CoV-2 virus has underscored the need for rapid, simple, scalable, and high-throughput multiplex diagnostics in non-laboratory settings. Here we demonstrate a multiplex reverse-transcription loop-mediated isothermal amplification (RT-LAMP) coupled with a gold nanoparticle-based lateral flow immunoassay (LFIA) capable of detecting up to three unique viral gene targets in 15 min. RT-LAMP primers associated with three separate gene targets from the SARS-CoV-2 virus (Orf1ab, Envelope, and Nucleocapsid) were added to a one-pot mix. A colorimetric change from red to yellow occurs in the presence of a positive sample. Positive samples are run through a LFIA to achieve specificity on a multiplex three-test line paper assay. Positive results are indicated by a characteristic crimson line. The device is almost fully automated and is deployable in any community setting with a power source.

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer.

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in-first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Non-Invasive Blood Flow Speed Measurement Using Optics.

Non-invasive measurement of the arterial blood speed gives important health information such as cardio output and blood supplies to vital organs. The magnitude and change in arterial blood speed are key indicators of the health conditions and development and progression of diseases. We demonstrated a simple technique to directly measure the blood flow speed in main arteries based on the diffused light model. The concept is demonstrated with a phantom that uses intralipid hydrogel to model the biological tissue and an embedded glass tube with flowing human blood to model the blood vessel. The correlation function of the measured photocurrent was used to find the electrical field correlation function via the Siegert relation. We have shown that the characteristic decorrelation rate (i.e., the inverse of the decoherent time) is linearly proportional to the blood speed and independent of the tube diameter. This striking property can be explained by an approximate analytic solution for the diffused light equation in the regime where the convective flow is the dominating factor for decorrelation. As a result, we have demonstrated a non-invasive method of measuring arterial blood speed without any prior knowledge or assumption about the geometric or mechanic properties of the blood vessels.

A 30.3 fA/√Hz Biosensing Current Front-End With 139 dB Cross-Scale Dynamic Range.

This paper presents an 8-channel array of low-noise (30.3 fA/√Hz) current sensing front-ends with on-chip microelectrode electrochemical sensors. The analog front-end (AFE) consists of a 1st-order continuous-time delta-sigma (CT ΔΣ) modulator that achieves 123 fA sensitivity over a 10 Hz bandwidth and 139 dB cross-scale dynamic range with a 2-bit programmable current reference. A digital predictor and tri-level pulse width modulated (PWM) current-steering DAC realize the equivalent performance of a multi-bit ΔΣ in an area- and power-efficient manner. The AFE consumes 50.3 µW and 0.11 mm2 per readout channel. The proposed platform was used to observe protein-ligand interactions in real-time using transient induced molecular electronic spectroscopy (TIMES), a label- and immobilization-free biosensing technique.

Minimizing Iridium Oxide Electrodes for High Visual Acuity Subretinal Stimulation.

Vision loss from diseases of the outer retina, such as age-related macular degeneration, is among the leading causes of irreversible blindness in the world today. The goal of retinal prosthetics is to replace the photo-sensing function of photoreceptors lost in these diseases with optoelectronic hardware to electrically stimulate patterns of retinal activity corresponding to vision. To enable high-resolution retinal prosthetics, the scale of stimulating electrodes must be significantly decreased from current designs; however, this reduces the amount of stimulating current that can be delivered. The efficacy of subretinal stimulation at electrode sizes suitable for high visual acuity retinal prosthesis are not well understood, particularly within the safe charge injection limits of electrode materials. Here, we measure retinal ganglion cell (RGC) responses in a mouse model of blindness to evaluate the stimulation efficacy of 10, 20, and 30 µm diameter iridium oxide electrodes within the electrode charge injection limits, focusing on measures of charge threshold and dynamic range. Stimulation thresholds were lower for smaller electrodes, but larger electrodes could elicit a greater dynamic range of spikes and recruited more ganglion cells within charge injection limits. These findings suggest a practical lower limit for planar electrode size and indicate strategies for maximizing stimulation thresholds and dynamic range.

Using airflow-driven, evaporative gradients to improve sensitivity and fluid control in colorimetric paper-based assays.

Microfluidic paper-based analytical devices (µPADs) are foundational devices for point-of-care testing, yet suffer from limitations in regards to their sensitivity and capability in handling complex assays. Here, we demonstrate an airflow-based, evaporative method that is capable of manipulating fluid flows within paper membranes to offer new functionalities for multistep delivery of reagents and improve the sensitivity of µPADs by 100-1000 times. This method applies an air-jet to a pre-wetted membrane, generating an evaporative gradient such that any solutes become enriched underneath the air-jet spot. By controlling the lateral position of this spot, the solutes in the paper strip are enriched and follow the air jet trajectory, driving the reactions and enhancing visualization for colorimetric readout in multistep assays. The technique has been successfully applied to drive the sequential delivery in multistep immunoassays as well as improve sensitivity for colorimetric detection assays for nucleic acids and proteins via loop-mediated isothermal amplification (LAMP) and ELISA. For colorimetric LAMP detection of the COVID-19 genome, enrichment of the solution on paper could enhance the contrast of the dye in order to more clearly distinguish between the positive and negative results to achieve a sensitivity of 3 copies of SARS-Cov-2 RNAs. For ELISA, enrichment of the oxidized TMB substrate yielded a sensitivity increase of two-to-three orders of magnitude when compared to non-enriched samples - having a limit of detection of around 200 fM for IgG. Therefore, this enrichment method represents a simple process that can be easily integrated into existing detection assays for controlling fluid flows and improving detection of biomarkers on paper.

Label-free image-encoded microfluidic cell sorter with a scanning Bessel beam.

The microfluidic-based, label-free image-guided cell sorter offers a low-cost, high information content, and disposable solution that overcomes many limitations in conventional cell sorters. However, flow confinement for most microfluidic devices is generally only one-dimensional using sheath flow. As a result, the equilibrium distribution of cells spreads beyond the focal plane of commonly used Gaussian laser excitation beams, resulting in a large number of blurred images that hinder subsequent cell sorting based on cell image features. To address this issue, we present a Bessel-Gaussian beam image-guided cell sorter with an ultra-long depth of focus, enabling focused images of >85% of passing cells. This system features label-free sorting capabilities based on features extracted from the output temporal waveform of a photomultiplier tube (PMT) detector. For the sorting of polystyrene beads, SKNO1 leukemia cells, and Scenedesmus green algae, our results indicate a sorting purity of 97%, 97%, and 98%, respectively, showing that the temporal waveforms from the PMT outputs have strong correlations with cell image features. These correlations are also confirmed by off-line reconstructed cell images from a temporal-spatial transformation algorithm tailored to the scanning Bessel-Gaussian beam.

A fabrication process for flexible single-crystal perovskite devices.

Organic-inorganic hybrid perovskites have electronic and optoelectronic properties that make them appealing in many device applications1-4. Although many approaches focus on polycrystalline materials5-7, single-crystal hybrid perovskites show improved carrier transport and enhanced stability over their polycrystalline counterparts, due to their orientation-dependent transport behaviour8-10 and lower defect concentrations11,12. However, the fabrication of single-crystal hybrid perovskites, and controlling their morphology and composition, are challenging12. Here we report a solution-based lithography-assisted epitaxial-growth-and-transfer method for fabricating single-crystal hybrid perovskites on arbitrary substrates, with precise control of their thickness (from about 600 nanometres to about 100 micrometres), area (continuous thin films up to about 5.5 centimetres by 5.5 centimetres), and composition gradient in the thickness direction (for example, from methylammonium lead iodide, MAPbI3, to MAPb0.5Sn0.5I3). The transferred single-crystal hybrid perovskites are of comparable quality to those directly grown on epitaxial substrates, and are mechanically flexible depending on the thickness. Lead-tin gradient alloying allows the formation of a graded electronic bandgap, which increases the carrier mobility and impedes carrier recombination. Devices based on these single-crystal hybrid perovskites show not only high stability against various degradation factors but also good performance (for example, solar cells based on lead-tin-gradient structures with an average efficiency of 18.77 per cent).

Characterizations of protein-ligand reaction kinetics by transistor-microfluidic integrated sensors.

Understanding the binding affinities and kinetics of protein-ligand interactions using a label-free method is crucial for identifying therapeutic candidates in clinical diagnostics and drug development. In this work, the IGZO-TFT (thin-film transistor) biosensor integrated with a tailored microfluidic chip was developed to explore binding kinetics of protein-ligand biochemical interactions in the real-time manner. The IGZO-TFT sensor extracts the binding characteristics through sensing biomolecules by their electrical charges. Using lysozyme and tri-N-acetyl-D-glucosamine (NAG3) as an example, we established a procedure to obtain the parameters, such as the dissociation constant, Kd, and association rate constant, ka, that are critical to biochemical reactions. The correlation between the lysozyme concentration and TFT drain current signal was first constructed. Next, solutions of lysozyme and NAG3 of different mixing ratios were prepared. They were pre-mixed for various periods of reaction time before applying to the TFT sensor to extract signals of lysozyme molecules and the concentration remaining. With the knowledge of drain current changes at different reaction times, ka and Kd can be obtained. The values from our experiment are comparable to other methods, which suggests the proposed approach can be employed to explore protein-ligand interaction kinetics in the massively parallel manner if the TFT array is considered.

Detecting Protein-Ligand Interaction from Integrated Transient Induced Molecular Electronic Signal (i-TIMES).

Quantitative information about protein-ligand interactions is central to drug discovery. To obtain the quintessential reaction dissociation constant, ideally measurements of reactions should be performed without perturbations by molecular labeling or immobilization. The technique of transient induced molecular electrical signal (TIMES) has provided a promising technique to meet such requirements, and its performance in a microfluidic environment further offers the potential for high throughput and reduced consumption of reagents. In this work, we further the development by using integrated TIMES signal (i-TIMES) to greatly enhance the accuracy and reproducibility of the measurement. While the transient response may be of interest, the integrated signal directly measures the total amount of surface charge density resulted from molecules near the surface of electrode. The signals enable quantitative characterization of protein-ligand interactions. We have demonstrated the feasibility of i-TIMES technique using different biomolecules including lysozyme, N,N',N″-triacetylchitotriose (TriNAG), aptamer, p-aminobenzamidine (pABA), bovine pancreatic ribonuclease A (RNaseA), and uridine-3'-phosphate (3'UMP). The results show i-TIMES is a simple and accurate technique that can bring tremendous value to drug discovery and research of intermolecular interactions.

Vertically integrated photo junction-field-effect transistor pixels for retinal prosthesis.

Optoelectronic retinal prostheses transduce light into electrical current for neural stimulation. We introduce a novel optoelectronic pixel architecture consisting of a vertically integrated photo junction-field-effect transistor (Photo-JFET) and neural stimulating electrode. Experimental measurements demonstrate that optically addressed Photo-JFET pixels utilize phototransistive gain to produce a broad range of neural stimulation current and can effectively stimulate retinal neurons in vitro. The compact nature of the Photo-JFET pixel can enable high resolution retinal prostheses with the smallest reported optoelectronic pixel size to help restore high visual acuity in patients with degenerative retinal diseases.