Elfriede Simon

Low-power gas sensors based on work-function measurement in low-cost hybrid flip–chip technology ☆

To implement low-power gas sensors with low component costs, the principle of work-function read out via a hybrid suspended gate FET (SGFET) is being pursued, whereby a freely selectable sensor film undergoes a reversible work-function change corresponding to the build-up of a potential difference on the surface in response to gas adsorption/reaction. This is read out via an ISFET structure. An innovative design which allows cheap manufacturing will be described for the principle that has already been successfully demonstrated. The starting point of the design is a ceramic Al2O3 substrate coated with conductor patterns and sensitive materials onto which the FET is mounted in flip–chip technology. By means of the freely selectable sensor film and its preparation method, a wide range of applications can be opened up.

Biological and chemical sensors for cancer diagnosis

The great challenge for sensor systems to be accepted as a relevant diagnostic and therapeutic tool for cancer detection is the ability to determine the presence of relevant biomarkers or biomarker patterns comparably to or even better than the traditional analytical systems. Biosensor and chemical sensor technologies are already used for several clinical applications such as blood glucose or blood gas measurements. However, up to now not many sensors have been developed for cancer-related tests because only a few of the biomarkers have shown clinical relevance and the performance of the sensor systems is not always satisfactory. New genomic and proteomic tools are used to detect new molecular signatures and identify which combinations of biomarkers may detect best the presence or risk of cancer or monitor cancer therapies. These molecular signatures include genetic and epigenetic signatures, changes in gene expressions, protein biomarker profiles and other metabolite profile changes. They provide new changes in using different sensor technologies for cancer detection especially when complex biomarker patterns have to be analyzed. To address requirements for this complex analysis, there have been recent efforts to develop sensor arrays and new solutions (e.g. lab on a chip) in which sampling, preparation, high-throughput analysis and reporting are integrated. The ability of parallelization, miniaturization and the degree of automation are the focus of new developments and will be supported by nanotechnology approaches. This review recaps some scientific considerations about cancer diagnosis and cancer-related biomarkers, relevant biosensor and chemical sensor technologies, their application as cancer sensors and consideration about future challenges.

Realization of a new sensor concept: improved CCFET and SGFET type gas sensors in Hybrid Flip-Chip technology

A wide range of emerging applications in the consumer sector is envisioned for low-cost gas sensors with a high degree of adaptability to special measuring applications. The readout of gas induced work function changes via hybrid suspended gate field effect devices is already accepted as a promising technique for the realisation of a versatile, low-cost sensor platform. However, the industrialisation is still in the beginning. We report the realisation of the innovative, cheaply manufacturable Hybrid Flip-Chip (HFCFET) setup which was introduced. Using different modifications of improved ISFET and CCFET type transducers we compare the performance of these readout devices. Additionally, the design of the readout structures was improved compared to other reported devices. Profiting from the use of a standard CMOS process an analogue readout circuit was integrated in a CCFET type transducer enabling an on-chip reference and compensation of signal drifts.

Fractional exhaled nitric oxide measurement with a handheld device.

A sensing system for fractional exhaled nitric oxide (FeNO) measurement is presented, which is characterized by a compact setup and a cost potential to be made available for the patient at home. The sensing is based on the work function measurement of a phthalocyanine-type sensing material, which is shown to be sufficiently sensitive for NO(2) in the ppb range. The transducer used to measure the work function is a field effect transistor with a suspended gate electrode. Selectivity is given with respect to other breath components including typically metabolic by-products. The measurement system includes breath treatments in a simple setup, which essentially are dehumidification and a quantitative conversion of NO to NO(2) with a conversion rate of approx. 95%, using a disposable oxidation catalyst. The accomplishment of the correct exhalation maneuver and feeding of the suited portion of exhaled air to the sensor is provided by breath sampling means. The sensor is not gas consuming. This allows us to fill the measurement chamber once, instead of establishing a gas flow for the measurement. This feature simplifies the device architecture. In this paper, we report on sensor characteristics, system architecture and measurement with artificial breath-gas as well as with human breath with the device.

Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery

Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information.

Towards an Efficient Computational Framework for Guiding Surgical Resection through Intra-operative Endo-microscopic Pathology

Precise detection and surgical resection of the tumors during an operation greatly increases the chance of the overall procedure efficacy. Emerging experimental in-vivo imaging technologies such as Confocal Laser Endomicroscopy CLE, could potentially assist surgeons to examine brain tissues on histological scale in real-time during the operation. However, it is a challenging task for neurosurgeons to interpret these images in real-time, primarily due to the low signal to noise ratio and variability in the patterns expressed within these images by various examined tissue types. In this paper, we present a comprehensive computational framework capable of automatic brain tumor classification in real-time. Specifically, our contributions include: a an end-to-end computational pipeline where a variety of the feature extraction methods, encoding schemes, and classification algorithms can be readily deployed, b thorough evaluation of state-of-the-art low-level image features and popular encoding techniques in context of CLE imagery, and finally, c A highly optimized feature pooling method based on codeword proximity. The proposed system can effectively classify two types of commonly diagnosed brain tumors in CLE sequences captured in real-time with close to 90% accuracy. Extensive experiments on a dataset of 117 videos demonstrate the efficacy of our system.

Novel p-dopant toward highly efficient and stable perovskite solar cells

Li-TFSI is the most common p-dopant for the hole conductor spiro-MeOTAD in the normal structure (n–i–p) of perovskite solar cells (PSCs), which consistently yield the highest power conversion efficiency (PCE) albeit at the risk of lower long-term operational stability. Here we successfully replace conventional Li-TFSI with Zn-TFSI2, which not only acts as a highly effective p-dopant but also enhances considerably both the photovoltaic performance and long-term stability. The incorporation of Zn-TFSI2 as a dopant for spiro-MeOTAD leads to an increase by one order in the hole mobility compared to Li-TFSI from 3.78 × 10−3 cm2 V−1 s−1 to 3.83 × 10−2 cm2 V−1 s−1. Furthermore, the device with Zn-TFSI2 showed an 80 mV higher built-in voltage and a bigger recombination resistance than the one with Li-TFSI, which were responsible for the striking increase in both the open-circuit voltage and fill factor, leading to a stabilized PCE of 22.0% for the best cells. Remarkably, the device employing Zn-TFSI2 demonstrated superb photo-stability, showing even a 2% increase in the PCE after 600 h light soaking at the maximum power point (mpp) under full sun, while the PCE of the device with Li-TFSI decreased by 20% under the same conditions. Similarly, the device with Zn-TFSI2 showed better operational stability at 50 °C resulting in a 21% decrease in the PCE after 100 h aging at the mpp under full sun while the Li-TFSI based one showed a 55% decrease. Moreover, the Zn-TFSI2 based device was capable of effectively resisting humidity compared to the one based on Li-TFSI from shelf stability monitoring (R.H. ≥ 40%) in the dark.