The objective measurement of subjective, multi-dimensionally experienced pain is a problem for which there has not been an adequate solution. Although verbal methods (e.g., pain scales and questionnaires) are commonly used to measure clinical pain, they tend to lack objectivity, reliability, or validity when applied to mentally impaired individuals. Biopotential and behavioral parameters may represent a solution. Such coding systems already exist, but they are either very costly or time-consuming or have not been sufficiently evaluated. In this context, we collected a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. One hundred thirty-five features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, and variability. The following features were chosen as the most selective: (1) electromyography corrugator peak to peak, (2) corrugator shannon entropy, and (3) heart rate variability slope RR. Individual-specific calibration allows the adjustment of feature patterns, resulting in significantly more accurate pain detection rates. The objective measurement of pain in patients will provide valuable information for the clinical team, which may aid the objective assessment of treatment (e.g., effectiveness of drugs for pain reduction, information on surgical indication, and quality of care provided to patients).

Poly(ether ether ketone) nanocomposites containing vapour-grown carbon nanofibres (CNF) were produced using standard polymer processing techniques. At high shear rates no significant increase in resin viscosity was observed. Nevertheless, the addition of the CNFs results in a higher melt strength at 360°C. Electron microscopy confirmed the homogeneous dispersion and alignment of nanofibres in the polymer matrix. Evaluation of the mechanical composite properties revealed a linear increase in tensile stiffness and strength with nanofibre loading fractions up to 15 wt% whilst matrix ductility was maintained up to 10 wt%. An interpretation of the composite performance by short-fibre theory resulted in rather low intrinsic stiffness properties of the vapour-grown CNF. Differential scanning calorimetry was used to investigate crystallization kinetics and degree of crystallinity. The CNFs were found not to act as nucleating sites. Furthermore, unidirectional sliding tests against two different counterpart materials (100Cr6 martensitic bearing steel, X5CrNi18-10 austenitic stainless steel) were performed. The carbon nanofibres were found to reduce the wear rate of PEEK significantly.