A 64-channel, 1.1-pA-accurate on-chip potentiostat for parallel electrochemical monitoring

Electrochemical monitoring is crucial for both industrial applications, such as microbial electrolysis and corrosion monitoring as well as consumer applications such as personal health monitoring. Yet, state-of-the-art integrated potentiostat monitoring devices have few parallel channels with limited flexibility due to their channel architecture. This work presents a novel, widely scalable channel architecture using a switch capacitor based Howland current pump and a digital potential controller. An integrated, 64-channel CMOS potentiostat array has been fabricated. Each individual channel has a dynamic current range of 120dB with 1.1pA precision with up to 100kHz bandwidth. The on-chip working electrodes are post-processed with gold to ensure (bio)electrochemical compatibility

An Adaptive Channel Selection scheme for Reliable TSCH-based Communication

Energy consumption is one of the main issues of Internet of things applications. The IEEE 802.15.4 standard defines the medium access control and physical layer for low datarate wireless personal area networks with low energy con- sumption. The limitations of IEEE 802.15.4 in reliability are overcome by the IEEE 802.15.4e standard amendment defining three more MAC modes. Time slotted channel hopping (TSCH) is the most interesting one, providing high reliability. However, the link quality degradation still exists in the TSCH protocol, especially with high amount of interference and deep fading. This comes from the fact that the channel list in TSCH is fixed. In this paper we propose an adaptive channel selection scheme based on the multi-arm bandit problem. The selection of each channel is formulated as an independent process with an associated variable called the Gittins index. For optimal channel selection, the algorithm is modified to adapt to a varying environment by using a greedy strategy. The trade off between exploring all the channels to gather information and exploiting the selected good channels to avoid interference or fading, is investigated to achieve the best performance. Network simulation is done to explore and verify the algorithm design with NS-3. The throughput of TSCH with our channel selection scheme can achieve 85% of the ideal case, while TSCH with default hopping list can only achieve 54% in our simulation scenario.status: publishe

A 64-channel, 1.1-pA-accurate On-chip Potentiostat for Parallel Electrochemical Monitoring

Electrochemical monitoring is crucial for both industrial applications, such as microbial electrolysis and corrosion monitoring as well as consumer applications such as personal health monitoring. Yet, state-of-the-art integrated potentiostat monitoring devices have few parallel channels with limited flexibility due to their channel architecture. This work presents a novel, widely scalable channel architecture using a switch capacitor based Howland current pump and a digital potential controller. An integrated, 64-channel CMOS potentiostat array has been fabricated. Each individual channel has a dynamic current range of 120dB with 1.1pA precision with up to 100kHz bandwidth. The on-chip working electrodes are post-processed with gold to ensure (bio)electrochemical compatibility.status: Published onlin

Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis

Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease that causes irreversible damage to lung tissue characterized by excessive deposition of extracellular matrix (ECM) and remodeling of lung parenchyma. The current diagnosis of IPF is complex and usually completed by a multidisciplinary team including clinicians, radiologists and pathologists they work together and make decision for an effective treatment, it is imperative to introduce novel practical methods for IPF diagnosis. This study provided a new diagnostic model of idiopathic pulmonary fibrosis based on machine learning. Six genes including CDH3, DIO2, ADAMTS14, HS6ST2, IL13RA2, and IGFL2 were identified based on the differentially expressed genes in IPF patients compare to healthy subjects through a random forest classifier with the existing gene expression databases. An artificial neural network model was constructed for IPF diagnosis based these genes, and this model was validated by the distinctive public datasets with a satisfactory diagnostic accuracy. These six genes identified were significant correlated with lung function, and among them, CDH3 and DIO2 were further determined to be significantly associated with the survival. Putting together, artificial neural network model identified the significant genes to distinguish idiopathic pulmonary fibrosis from healthy people and it is potential for molecular diagnosis of IPF