Detection of some amino acids with modulation-doped and surface-nanoengineered GaAs Schottky P-I-N diodes

Most current techniques for analyzing amino acids require substantial instrumentation and significant sample preprocessing. In this study, we designed, fabricated, and tested a scalable diode-based microdevice that allows for direct sensing of amino acids. The device is based on modulation-doped GaAs heterostructure with a Schottky contact on one side. The relatively high mobility and relatively small dielectric constant of GaAs are naturally helpful in this problem. We also paid attention to a proper etching procedure allowing for substantial modification of the surface properties, thereby further boosting the sensing performance. Transport data (I-V, differential conductance) are presented for three qualitatively different classes of amino acids (i.e., nonpolar with aliphatic R-group, polar uncharged R-group, and charged R-group) with glycine, cysteine, and histidine as specific examples, respectively. The conductance for the GaAs-amino acid interface measured using a scanning tunneling microscope (STM) was previously reported to have distinct spectral features. In this paper, we show that measuring the differential conductance of a GaAs diode, whose surface is in direct contact with an aqueous solution of amino acid, is a simple methodology to access useful information, previously available only through sophisticated and equipment-demanding STM and molecular electronics approaches. Density functional theory calculations were used to examine which adsorption processes were likely responsible for the observed surface conductance modification. Last, in future and ongoing work, we illustrate how it might be possible to employ standard multivariate data analysis techniques to reliably identify distinct (95%) single amino acid specific features in near-ambient differential conductance data

Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains

Anchored in the resource‐based view theory, the objective of this research is to empirically analyse the behavioural factors affecting the green supply chain management (GCSM) performance in a fast‐growing emerging economy by taking an empirical data set of 101 responses from personnel in the mining sector. Behavioural factors in green supply chains are still a critical challenge—not yet a well‐explored academic subject—when the focus is on the mining industry of emerging economies like India; the lack of studies in this field could be a factor preventing the Indian mining industry becoming more green. In terms of methodology, original survey data were processed through AMOS 4.0, adopted for assessing the causal connection among the six constructs, that is, top management support, teamwork, workplace culture, resistance to change, green innovation, and green motivation. We further explore the input from the human side of GCSM by highlighting that top management support and green motivation are the most crucial behavioural factors that influence GCSM in the Indian mining sector. The study will be helpful for mining companies because it will enable them to identify the areas that require their attention for enhancing GCSM performance related to behavioural aspects