Self-Cleaning Mechanism: Why Nanotexture and Hydrophobicity Matter.

Dust particles can adhere to surfaces, thereby decreasing the efficiency of diverse processes, such as light absorption by solar panels. It is well known that superhydrophobicity reduces the friction between water droplets and the surface, thus allowing water drops to slide/roll and detach (clean) particles from surfaces. However, the forces that attach and detach particles from surfaces during the self-cleaning mechanism and the effect of nanotextures on these forces are not fully understood. To shed light on these forces and the effect of nanotexture on them, we prepared four Si-based samples (relevant to solar panels): (1) smooth or (2) nanotextured hydrophilic surfaces and (3) smooth or (4) nanotextured hydrophobic surfaces. In agreement with previous publications, it is shown that the efficiency of particle removal increases with hydrophobicity. Furthermore, nanotexture enhances the hydrophobicity, whereby particle removal is further increased. Specifically, hydrophilic particle removal increased from ∼41%, from hydrophilic smooth Si wafers to 98% from superhydrophobic Si-based nanotextured surfaces. However, the reason for the increased particle removal is not low friction between the droplets and the superhydrophobic surfaces; it is the reduction of the adhesion force between the particle and the surface and the altered geometry of the water-particle-air line tension acting on the particles on superhydrophobic surfaces, which increases the force that can detach particles from the surfaces. The experimental methods we used and the criterion for particle removal we derived can be implemented to engineer self-cleaning surfaces using other surfaces and dust particles, exhibiting different chemistries and/or textures.

From sensing interactions to controlling the interactions: a novel approach to obtain biological transistors for specific and label-free immunosensing.

Antibody-antigen interactions are shaped by the solution pH level, ionic strength, and electric fields, if present. In biological field-effect transistors (BioFETs), the interactions take place at the sensing area in which the pH level, ionic strength and electric fields are determined by the Poisson-Boltzmann equation and the boundary conditions at the solid-solution interface and the potential applied at the solution electrode. The present study demonstrates how a BioFET solution electrode potential affects the sensing area double layer pH level, ionic strength, and electric fields and in this way shapes the biological interactions at the sensing area. We refer to this as 'active sensing'. To this end, we employed the meta-nano-channel (MNC) BioFET and demonstrate how the solution electrode can determine the antibody-antigen equilibrium constant and allows the control and tuning of the sensing performance in terms of the dynamic range and limit-of-detection. In the current work, we employed this method to demonstrate the specific and label-free sensing of Alpha-Fetoprotein (AFP) molecules from 0.5 µL drops of 1 : 100 diluted serum. AFP was measured during pregnancy as part of the prenatal screening program for fetal anomalies, chromosomal abnormalities, and abnormal placentation. We demonstrate AFP sensing with a limit-of-detection of 10.5 aM and a dynamic range of 6 orders of magnitude in concentration. Extensive control measurements are reported.

The non-stationary case of the Maxwell-Garnett theory: growth of nanomaterials (2D gold flakes) in solution.

The solution-based growth mechanism is a common process for nanomaterials. The Maxwell-Garnett theory (for light-matter interactions) describes the solution growth in an effective medium, homogenized by a mean electromagnetic field, which applies when materials are in a stationary phase. However, the charge transitions (inter- and intra-transitions) during the growth of nanomaterials lead to a non-stationary phase and are associated with time-dependent permittivity constant transitions (for nanomaterials). Therefore, time-independence in the standard Maxwell-Garnett theory is lost, resulting in time dependence, ε i(t). This becomes important when the optical spectrum of a solution needs to be deconvoluted at different reaction times since each peak represents a specific charge/energy transfer with a specific permittivity constant. Based on this, we developed a time-resolved deconvolution approach, f(t) ∝ ε i(t), which led us to identify the transitions (inter- and intra-transitions) with their dominated growth regimes. Two gold ion peaks were precisely measured (322 nm and 367 nm) for the inter-transition, and three different polyaniline oxidation states (PAOS) for the intra-transition, including A (372 nm), B (680 nm), and C (530 nm). In the initial reaction time regime (0-90 min), the permittivity constant of gold was found to be highly dependent on time, i.e. f E ∝ ε i(t), since charge transfer takes place from the PAOS to gold ions (i.e. inter-transition leads to a reduction reaction). In the second time regime (90-180 min), the permittivity constant of gold changes as the material deforms from 3D to 2D (f S ∝ ε 3D-2D), i.e. intra-transition (combined with thermal reduction). Our approach provides a new framework for the time-dependent modelling of (an)isotropic solutions of other nanomaterials and their syntheses.