Structure-activity relationship of graphene-related materials: A meta-analysis based on mammalian in vitro toxicity data.

To support a safe application of graphene-related materials (GRMs) it is necessary to understand the potential negative impacts they could have on human health, in particular on the lung - one of the most sensitive exposure routes. Machine learning (ML) approaches can help analyse the results of multiple toxicity studies to understand the structure-activity relationship and the effect of experimental conditions, thus supporting predictive nanotoxicology. In this work we collected in vitro cytotoxicity data obtained from studies using lung cells; we then fitted multiple regression models to predict this endpoint based on the material properties and experimental conditions. Moreover, the data set was used to calculate the Benchmark Dose Lower Confidence Interval (BMDL), a dose descriptor widely used in risk assessment. Regression and classification models were applied for the prediction of the BMDL value and BMDL range. The analyses show that both cytotoxicity and the BMDL range can be predicted well (Q2 = 0.77 and accuracy = 0.71, respectively). Both physico-chemical characteristics such as the lateral size, number of layers, and functionalization, and experimental conditions such as the assay and media used were important predicting features, confirming the need for thorough characterization and reporting of these parameters.

Counting the number of enzymes immobilized onto a nanoparticle-coated electrode.

To immobilize enzymes at the surface of a nanoparticle-based electrochemical sensor is a common method to construct biosensors for non-electroactive analytes. Studying the interactions between the enzymes and nanoparticle support is of great importance in optimizing the conditions for biosensor design. This can be achieved by using a combination of analytical methods to carefully characterize the enzyme nanoparticle coating at the sensor surface while studying the optimal conditions for enzyme immobilization. From this analytical approach, it was found that controlling the enzyme coverage to a monolayer was a key factor to significantly improve the temporal resolution of biosensors. However, these characterization methods involve both tedious methodologies and working with toxic cyanide solutions. Here we introduce a new analytical method that allows direct quantification of the number of immobilized enzymes (glucose oxidase) at the surface of a gold nanoparticle coated glassy carbon electrode. This was achieved by exploiting an electrochemical stripping method for the direct quantification of the density and size of gold nanoparticles coating the electrode surface and combining this information with quantification of fluorophore-labeled enzymes bound to the sensor surface after stripping off their nanoparticle support. This method is both significantly much faster compared to previously reported methods and with the advantage that this method presented is non-toxic. Graphical abstract A new analytical method for direct quantification of the number of enzymes immobilized at the surface of gold nanoparticles covering a glassy carbon electrode using anodic stripping and fluorimetry.

Lithographic Microfabrication of a 16-Electrode Array on a Probe Tip for High Spatial Resolution Electrochemical Localization of Exocytosis.

We report the lithographic microfabrication of a movable thin film microelectrode array (MEA) probe consisting of 16 platinum band electrodes placed on top of a supporting borosilicate glass substrate. These 1.2 µm wide electrodes were tightly packed and positioned parallel in two opposite rows within a 20 µm × 25 µm square area and with a distance less than 10 µm from the edge of the glass substrate. We demonstrate the ability to control and place the probe in close proximity to the surface of adherent bovine chromaffin cells and to amperometrically record single exocytosis release events with high spatiotemporal resolution. The two-dimensional position of single exocytotic events occurring in the center gap area separating the two rows of MEA band electrodes and that were codetected by electrodes in both rows was determined by analysis of the fractional detection of catecholamine released between electrodes and exploiting random walk simulations. Hence, two-dimensional electrochemical imaging recording of exocytosis release between the electrodes within this area was achieved. Similarly, by modeling the current spikes codetected by parallel adjacent band electrodes positioned in the same electrode row, a one-dimensional imaging of exocytosis with submicrometer resolution was accomplished within the area. The one- and two-dimensional electrochemical imaging using the MEA probe allowed for high spatial resolution of exocytosis activity and revealed heterogeneous release of catecholamine at the chromaffin cell surface.

Characterizing the catecholamine content of single mammalian vesicles by collision-adsorption events at an electrode.

We present the electrochemical response to single adrenal chromaffin vesicles filled with catecholamine hormones as they are adsorbed and rupture on a 33 µm diameter disk-shaped carbon electrode. The vesicles adsorb onto the electrode surface and sequentially spread out over the electrode surface, trapping their contents against the electrode. These contents are then oxidized, and a current (or amperometric) peak results from each vesicle that bursts. A large number of current transients associated with rupture of single vesicles (86%) are observed under the experimental conditions used, allowing us to quantify the vesicular catecholamine content.

Amperometric detection of single vesicle acetylcholine release events from an artificial cell.

Acetylcholine is a highly abundant nonelectroactive neurotransmitter in the mammalian central nervous system. Neurochemical release occurs on the millisecond time scale, requiring a fast, sensitive sensor such as an enzymatic amperometric electrode. Typically, the enzyme used for enzymatic electrochemical sensors is applied in excess to maximize signal. Here, in addition to sensitivity, we have also sought to maximize temporal resolution, by designing a sensor that is sensitive enough to work at near monolayer enzyme coverage. Reducing the enzyme layer thickness increases sensor temporal resolution by decreasing the distance and reducing the diffusion time for the enzyme product to travel to the sensor surface for detection. In this instance, the sensor consists of electrodeposited gold nanoparticle modified carbon fiber microelectrodes (CFMEs). Enzymes often are sensitive to curvature upon surface adsorption; thus, it was important to deposit discrete nanoparticles to maintain enzyme activity while depositing as much gold as possible to maximize enzyme coverage. To further enhance sensitivity, the enzymes acetylcholinesterase (AChE) and choline oxidase (ChO) were immobilized onto the gold nanoparticles at the previously determined optimal ratio (1:10 AChE/ChO) for most efficient sequential enzymatic activity. This optimization approach has enabled the rapid detection to temporally resolve single vesicle acetylcholine release from an artificial cell. The sensor described is a significant advancement in that it allows for the recording of acetylcholine release on the order of the time scale for neurochemical release in secretory cells.