Identifying the charge density and dielectric environment of graphene using Raman spectroscopy and deep learning.

The impact of the environment on the properties of graphene such as strain, charge density, and dielectric environment can be evaluated by Raman spectroscopy. These environmental interactions are not trivial to determine since they affect the spectra in overlapping ways. Data pre-processing such as background subtraction and peak fitting is typically used. Moreover, collected spectroscopic data vary due to different experimental setups and environments. Such variations, artifacts, and environmental differences pose a challenge for accurate spectral analysis. In this work, we developed a deep learning model to overcome the effects of such variations and classify graphene Raman spectra according to different charge densities and dielectric environments. We consider two approaches: deep learning models and machine learning algorithms to classify spectra with slightly different charge densities or dielectric environments. These two approaches show similar success rates for high signal-to-noise data. However, deep learning models are less sensitive to noise. To improve the accuracy and generalization of all models, we use data augmentation through additive noise and peak shifting. We demonstrated the spectral classification with 99% accuracy using a convolutional neural net (CNN) model. The CNN model can classify Raman spectra of graphene with different charge doping levels and even subtle variations in the spectra of graphene on SiO2 and graphene on silanized SiO2. Our approach has the potential for fast and reliable estimation of graphene doping levels and dielectric environments. The proposed model paves the way for achieving efficient analytical tools to evaluate the properties of graphene.

Broadband micro-transient absorption spectroscopy enabled by improved lock-in amplification.

Recent breakthroughs in material development have increased the demand for characterization methods capable of probing nanoscale features on ultrafast time scales. As the sample reduces to atomically thin levels, an extremely low-level signal limits the feasibility of many experiments. Here, we present an affordable and easy-to-implement solution to expand the maximum sensitivity of lock-in detection systems used in transient absorption spectroscopy by multiple orders of magnitude. By implementation of a tuned RC circuit to the output of an avalanche photodiode, electric pulse shaping allows for vastly improved lock-in detection. Furthermore, a carefully designed "peak detector" circuit provides additional pulse shaping benefits, resulting in even more lock-in detection signal enhancement. We demonstrate the improvement of lock-in detection with each of these schemes by performing benchmark measurements of a white-light continuum signal and micro-transient absorption spectroscopy on a few-layer transition metal dichalcogenide sample. Our results show the practicality of ultrafast pump-probe spectroscopy for many high-sensitivity experimental schemes.

Improving Performance of Tin-Doped-Zinc-Oxide Thin-Film Transistors by Optimizing Channel Structure.

In this paper, we investigated the performance of thin-film transistors (TFTs) with different channel configurations including single-active-layer (SAL) Sn-Zn-O (TZO), dual-active-layers (DAL) In-Sn-O (ITO)/TZO, and triple-active-layers (TAL) TZO/ITO/TZO. The TAL TFTs were found to combine the advantages of SAL TFTs (a low off-state current) and DAL TFTs (a high mobility and a low threshold voltage). The proposed TAL TFTs exhibit superior electrical performance, e.g. a high on-off state current ratio of 2 × 108, a low threshold voltage of 0.63 V, a high field effect mobility of 128.6 cm2/Vs, and a low off-state current of 3.3 pA. The surface morphology and characteristics of the ITO and TZO films were investigated and the TZO film was found to be C-axis-aligned crystalline (CAAC). A simplified resistance model was deduced to explain the channel resistance of the proposed TFTs. At last, TAL TFTs with different channel lengths were also discussed to show the stability and the uniformity of our fabrication process. Owing to its low-processing temperature, superior electrical performance, and low cost, TFTs with the proposed TAL channel configuration are highly promising for flexible displays where the polymeric substrates are heat-sensitive and a low processing temperature is desirable.

High-throughput and label-free parasitemia quantification and stage differentiation for malaria-infected red blood cells.

This work reports a high throughput and label-free microfluidic cell deformability sensor for quantitative parasitemia measurement and stage determination for Plasmodium falciparum-infected red blood cells (Pf-iRBCs). The sensor relies on differentiating the RBC deformability (a mechanical biomarker) that is highly correlated with the infection status. The cell deformability is measured by evaluating the transit time when each individual RBC squeezes through a microscale constriction (cross-section ~5µm×5µm). More than 30,000 RBCs can be analyzed for parasitemia quantification in under 1min with a throughput ~500 cells/s. Moreover, the device can also differentiate various malaria stages (ring, trophozoite, and schizont stage) due to their varied deformability. Using Pf-iRBCs at 0.1% parasitemia as a testing sample, the microfluidic deformability sensor achieved an excellent sensitivity (94.29%), specificity (86.67%) and accuracy (92.00%) in a blind test, comparable to the gold standard of the blood smear microscopy. As a supplement technology to the microscopy and flow cytometry, the microfluidic deformability sensor would possibly allow for label-free, rapid and cost-effective parasitemia quantification and stage determination for malaria in remote regions.

Arbitrarily Accessible 3D Microfluidic Device for Combinatorial High-Throughput Drug Screening.

Microfluidics-based drug-screening systems have enabled efficient and high-throughput drug screening, but their routine uses in ordinary labs are limited due to the complexity involved in device fabrication and system setup. In this work, we report an easy-to-use and low-cost arbitrarily accessible 3D microfluidic device that can be easily adopted by various labs to perform combinatorial assays for high-throughput drug screening. The device is capable of precisely performing automatic and simultaneous reagent loading and aliquoting tasks and performing multistep assays with arbitrary sequences. The device is not intended to compete with other microfluidic technologies regarding ultra-low reaction volume. Instead, its freedom from tubing or pumping systems and easy operation makes it an ideal platform for routine high-throughput drug screening outside traditional microfluidic labs. The functionality and quantitative reliability of the 3D microfluidic device were demonstrated with a histone acetyltransferase-based drug-screening assay using the recombinant Plasmodium falciparum GCN5 enzyme, benchmarked with a traditional microtiter plate-based method. This arbitrarily accessible, multistep capable, low-cost, and easy-to-use device can be widely adopted in various combinatorial assays beyond high-throughput drug screening.