Narrowing the Parameter Space of Collapse Models with Ultracold Layered Force Sensors

Despite the unquestionable empirical success of quantum theory, witnessed by the recent uprising of quantum technologies, the debate on how to reconcile the theory with the macroscopic classical world is still open. Spontaneous collapse models are one of the few testable solutions so far proposed. In particular, the continuous spontaneous localization (CSL) model has become subject of intense experimental research. Experiments looking for the universal force noise predicted by CSL in ultrasensitive mechanical resonators have recently set the strongest unambiguous bounds on CSL. Further improving these experiments by direct reduction of mechanical noise is technically challenging. Here, we implement a recently proposed alternative strategy that aims at enhancing the CSL noise by exploiting a multilayer test mass attached on a high quality factor microcantilever. The test mass is specifically designed to enhance the effect of CSL noise at the characteristic length r_{c}=10^{-7}  m. The measurements are in good agreement with pure thermal motion for temperatures down to 100 mK. From the absence of excess noise, we infer a new bound on the collapse rate at the characteristic length r_{c}=10^{-7}  m, which improves over previous mechanical experiments by more than 1 order of magnitude. Our results explicitly challenge a well-motivated region of the CSL parameter space proposed by Adler.

Bolometric bounds on the antineutrino mass

High statistics calorimetric measurements of the beta spectrum of 187Re are being performed with arrays of silver perrhenate crystals operated at low temperature. After a substantial modification of the experimental setup, a new measurement with ten silver perrhenate microbolometers has been running since July 2002. The crystals have masses around 300 microg and their average FWHM energy resolution is of 28.3 eV at the beta end point. The Kurie plot collected during 4485 h x mg effective running time has an end-point energy of 2466.1+/-0.8(stat)+/-1.5(syst) eV, while the half lifetime of the decay is found to be 43.2+/-0.2(stat)+/-0.1(syst) Gy. These values are the most precise obtained so far for 187Re. The best fit value for m(2)(nu(e)) is 147+/-237(stat)+/-90(syst) eV(2), which corresponds to an upper limit for the electron antineutrino mass m(nu(e))< or =21.7 eV at 90% C.L.

Development of ISFET array-based microsystems for bioelectrochemical measurements of cell populations

Monitoring the bioelectrochemical activity of living cells with sensor array-based microsystems represents an emerging technique in a large area of biomedical applications, ranging from basic research to various fields of pharmacological analyses. The main appeal is the ability of these miniaturised microsystems to perform, in real time, non-invasive in-vitro investigations of the physiological state of a cell population. In this paper, we present two different microsystems designed for multisite monitoring of the physiological state of a cell population. The first microsystem, intended for cellular metabolism monitoring, consists of an array of 12 spatially distributed ISFETs to detect small pH variations induced by the cell population. The second microsystem consists of an array of 40 ISFETs and 20 gold microelectrodes and it has been designed to monitor the electrical activity of neurons. This is achieved by direct coupling of the neuronal culture with the ISFET sensitive layer and by utilising gold microelectrodes for neuronal electrical stimulation.

A simple microfluidic system for patterning populations of neurons on silicon micromachined substrates

The purpose of this paper is to describe a low-cost simple technique based on the hydraulically driven deposition of adhesion molecules for patterning populations of neurons on silicon micromachined substrates. First, the design and fabrication process of the silicon micromachined substrates and the design of a flow-through chamber for the localised deposition of adhesive proteins are described. The experimental protocol for the deposition of the adhesive proteins is then presented. Finally, the results of experiments of 'entrapment' of chick embryo spinal cord neurons in microstructures of the silicon substrates and of formation of patterned biological neural networks are shown.