Ji Ge

Recent advances in nanorobotic manipulation inside scanning electron microscopes

A scanning electron microscope (SEM) provides real-time imaging with nanometer resolution and a large scanning area, which enables the development and integration of robotic nanomanipulation systems inside a vacuum chamber to realize simultaneous imaging and direct interactions with nanoscaled samples. Emerging techniques for nanorobotic manipulation during SEM imaging enable the characterization of nanomaterials and nanostructures and the prototyping/assembly of nanodevices. This paper presents a comprehensive survey of recent advances in nanorobotic manipulation, including the development of nanomanipulation platforms, tools, changeable toolboxes, sensing units, control strategies, electron beam-induced deposition approaches, automation techniques, and nanomanipulation-enabled applications and discoveries. The limitations of the existing technologies and prospects for new technologies are also discussed.

A System for Counting Fetal and Maternal Red Blood Cells

The Kleihauer-Betke (KB) test is the standard method for quantitating fetal-maternal hemorrhage in maternal care. In hospitals, the KB test is performed by a certified technologist to count a minimum of 2000 fetal and maternal red blood cells (RBCs) on a blood smear. Manual counting suffers from inherent inconsistency and unreliability. This paper describes a system for automated counting and distinguishing fetal and maternal RBCs on clinical KB slides. A custom-adapted hardware platform is used for KB slide scanning and image capturing. Spatial-color pixel classification with spectral clustering is proposed to separate overlapping cells. Optimal clustering number and total cell number are obtained through maximizing cluster validity index. To accurately identify fetal RBCs from maternal RBCs, multiple features including cell size, roundness, gradient, and saturation difference between cell and whole slide are used in supervised learning to generate feature vectors, to tackle cell color, shape, and contrast variations across clinical KB slides. The results show that the automated system is capable of completing the counting of over 60 000 cells (versus ~2000 by technologists) within 5 min (versus ~15 min by technologists). The throughput is improved by approximately 90 times compared to manual reading by technologists. The counting results are highly accurate and correlate strongly with those from benchmarking flow cytometry measurement.

Microfluidic Assessment of Frying Oil Degradation

Monitoring the quality of frying oil is important for the health of consumers. This paper reports a microfluidic technique for rapidly quantifying the degradation of frying oil. The microfluidic device generates monodispersed water-in-oil droplets and exploits viscosity and interfacial tension changes of frying oil samples over their frying/degradation process. The measured parameters were correlated to the total polar material percentage that is widely used in the food industry. The results reveal that the steady-state length of droplets can be used for unambiguously assessing frying oil quality degradation.

A Stick-Slip Positioning Stage Robust to Load Variations

In stick-slip positioning, friction between the stick-slip surfaces plays a critical role in positioning accuracy. Positioning performance varies when the load to drive changes. In this paper, by using a form-closed cam, the driving unit is separated from the moving unit to eliminate load influence. The stage is capable of operating in either a stepping mode or a scanning mode. Numerical modeling was conducted to analyze the static and dynamic characteristics of the stage design. In experiments, a number of parameters were tested on the constructed stage, and positioning performance was measured via laser-doppler vibrometry. Experimental results demonstrate that in the stepping mode, the stage has a travel range of 2 mm with incremental step sizes ranging from 30 nm to 2.3 μm. In the scanning mode, the stage has a positioning resolution of 1.15 nm. The measured results also confirm that load variations on the stage have little influence on contact friction force and positioning performance.

A System for Automated Detection of Ampoule Injection Impurities

Ampoule injection is a routinely used treatment in hospitals due to its rapid effect after intravenous injection. During manufacturing, tiny foreign particles can be present in the ampoule injection. Therefore, strict inspection must be performed before ampoule injections can be sold for hospital use. In the quality control inspection process, most ampoule enterprises still rely on manual inspection which suffers from inherent inconsistency and unreliability. This paper reports an automated system for inspecting foreign particles within ampoule injections. A custom-designed hardware platform is applied for ampoule transportation, particle agitation, and image capturing and analysis. Constructed trajectories of moving objects within liquid are proposed for use to differentiate foreign particles from air bubbles and random noise. To accurately classify foreign particles, multiple features including particle area, mean gray value, geometric invariant moments, and wavelet packet energy spectrum are used in supervised learning to generate feature vectors. The results show that the proposed algorithm is effective in classifying foreign particles and reducing false positive rates. The automated inspection system inspects over 150 ampoule injections per minute (versus

Automated Machine Vision System for Liquid Particle Inspection of Pharmaceutical Injection

\sim {12}~{\rm ampoule~ injections ~per~ minute}

A microfluidic system for assessing frying oil quality

by technologist) with higher accuracy and repeatability. In addition, the automated system is capable of diagnosing impurity types while existing inspection systems are not able to classify detected particles.

A system for automated counting of fetal and maternal red blood cells in clinical KB test

The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and effective inspection machine consisting of three modules, a mechanical system with 120 carousel grips, an image acquisition system with multihigh resolution cameras and a multilight sources station, and a distributed industrial electrical computer control system. Particle visual inspection machine first acquires image sequence using the high-speed image acquisition system. The image capture process at each camera module is alternately synchronized with different LED illumination techniques (light transmission method and light reflection method), enabling independent capture of particle images from the same container. Then, a set of novel algorithms for image registration and fast segmentation are proposed to minimize false rejections even in sensitive conditions, which enable the identification of all the tiny potential defects. Finally, a particle tracking and classification algorithm based on an adaptive local weighted-collaborative sparse model is also presented. The experiments demonstrate that the proposed inspection system can effectively detect the particles in the pharmaceutical infusion solution online, and achieve a performance rate of above 97% average accuracy.

Fluorescence and SEM correlative microscopy for nanomanipulation of subcellular structures

This paper reports a microfluidic system for assessing frying oil degradation. The hydrodynamic focusing microfluidic device developed for this purpose generates water-in-oil droplets. By correlating droplet size and the widely accepted oil quality metric, the total polar compound (TPM), it was proven that the device is capable of evaluating frying oil degradation. Compared to existing bulky instruments and methods, this device and measurement technique are fast, simple, and cost-effective.

Mechanical differences of sickle cell trait (SCT) and normal red blood cells

The Kleihauer-Betke test (KBT) is a widely used method for measuring fetal-maternal hemorrhage (FMH) in maternal care. In hospitals, KBT is performed by a certified technologist to count a minimum of 2,000 fetal and maternal red blood cells (RBCs) on a blood smear. Manual counting is inherently inconsistent and subjective. This paper presents a system for automated counting and distinguishing fetal and maternal RBCs on clinical KB slides. A custom-adapted hardware platform is used for KB slide scanning and image capturing. Spatial-color pixel classification with spectral clustering is proposed to separate overlapping cells. Optimal clustering number and total cell number are obtained through maximizing cluster validity index. To accurately identify fetal RBCs from maternal RBCs, multiple features including cell size, shape, gradient and saturation difference are used in supervised learning to generate feature vectors, to tackle cell color, shape and contrast variations across clinical KB slides. The results show that the automated system is capable of completing the counting of over 60,000 cells (vs. 2,000 by technologists) within 5 minutes (vs. 15 minutes by technologists). The counting results are highly accurate and correlate strongly with those from benchmarking flow cytometry measurement.