Conformal Titanium Nitride in a Porous Silicon Matrix: a Nanomaterial for In-Chip Supercapacitors

Today's supercapacitor energy storages are typically discrete devices aimed for printed boards and power applications. The development of autonomous sensor networks and wearable electronics and the miniaturisation of mobile devices would benefit substantially from solutions in which the energy storage is integrated with the active device. Nanostructures based on porous silicon (PS) provide a route towards integration due to the very high inherent surface area to volume ratio and compatibility with microelectronics fabrication processes. Unfortunately, pristine PS has limited wettability and poor chemical stability in electrolytes and the high resistance of the PS matrix severely limits the power efficiency. In this work, we demonstrate that excellent wettability and electro-chemical properties in aqueous and organic electrolytes can be obtained by coating the PS matrix with an ultra-thin layer of titanium nitride by atomic layer deposition. Our approach leads to very high specific capacitance (15 F/cm$^3$), energy density (1.3 mWh/cm$^3$), power density (up to 214 W/cm$^3$) and excellent stability (more than 13,000 cycles). Furthermore, we show that the PS-TiN nanomaterial can be integrated inside a silicon chip monolithically by combining MEMS and nanofabrication techniques. This leads to realisation of in-chip supercapacitor, i.e., it opens a new way to exploit the otherwise inactive volume of a silicon chip to store energy

Yleinen talouskehitys ohjaa kiertotalousinvestointien painotuksia

Non peer reviewe

Comparison of methodologies to estimate state-of-health of commercial Li-ion cells from electrochemical frequency response data

Various impedance-based and nonlinear frequency response-based methods for determining the state-of-health (SOH) of commercial lithium-ion cells are evaluated. Frequency response-based measurements provide a spectral representation of dynamics of underlying physicochemical processes in the cell, giving evidence about its internal physical state. The investigated methods can be carried out more rapidly than controlled full discharge and thus constitute prospectively more efficient measurement procedures to determine the SOH of aged lithium-ion cells. We systematically investigate direct use of electrochemical impedance spectroscopy (EIS) data, equivalent circuit fits to EIS, distribution of relaxation times analysis on EIS, and nonlinear frequency response analysis. SOH prediction models are developed by correlating key parameters of each method with conventional capacity measurement (i.e., current integration). The practical feasibility, reliability and uncertainty of each of the established SOH models are considered: all models show average RMS error in the range 0.75%–1.5% SOH units, attributable principally to cell-to-cell variation. Methods based on processed data (equivalent circuit, distribution of relaxation times) are more experimentally and numerically demanding but show lower average uncertainties and may offer more flexibility for future application