Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.

The need for biocompatible, low impedance abio-to-bio interfaces suitable for implantable bioactive devices has led us to develop electroconductive hydrogels. Conductive electroactive polymers (CEPs) are one dimensional organic semiconductors synthesized from aromatic monomers into extended pi-conjugated polymers. The synthesis of CEP’s, in association with hydrogels, produces a class of hybrid materials with the conductivity of the CEP and inherent biocompatibility of the hydrogel. CEPs provide a means for the additive electrodeposition of biorecognition molecules to specific nano- and micron-dimensioned metallic and semiconducting sites on microfabricated metallic or semiconductor MEMS devices. Through judicious engineering of repeat unit chemistry, polymer architecture, crosslink density and microstructure, the mechanical, transport and surface properties may be controlled. Implanted, these materials enable programmed low voltage electro-release of factors suitable for mitigating inflammatory response. Finally, these hydrogels can accommodate supramolecular assemblies of CNTs and enzymes supporting direct electron transfer for generation-3 biosensors and implantable biofuel cells.

Generation-3 (Gen-3) biosensors and advanced enzyme biofuel cells will benefit from direct electron transfer to oxidoreductases facilitated by single-walled carbon nanotubes (SWNTs). Supramolecular conjugates of SWNT-glucose oxidase (GOx-SWNT) were produced via ultrasonic processing. Using a Plackett-Burman experimental design to investigate the process of tip ultrasonication (23 kHz), conjugate formation was investigated as a function of ultrasonication times (0, 5, 60 min) and functionalized SWNTs of various tube lengths (SWNT-X-L), (X = −OH or -COOH and L = 3.0 μm, 7.5 μm). Enzyme activity (KM, kcat, kcat/KM, vmax and n (the Hill parameter)) of pGOx (pristine), sGOx (sonicated) and GOx-SWNT-X-L revealed that sonication of any duration increased both KM and kcat of GOx but did not change kcat/KM. Functionalized tubes had the most dramatic effect, reducing both KM and kcat and reducing kcat/KM. UV–vis spectra over the range of 300 to 550 nm of native enzyme-bound FAD (λmax at 381 and 452 nm) or the blue-shifted solvated FAD of the denatured enzyme (λmax at 377 and 448 nm) revealed that ultrasonication up to 60 minutes had no influence on spectral characteristics of FAD but that the longer SWNTs caused some partial denaturation leading to egress of FAD. Circular dichroism spectral analysis of the 2° structure showed that sonication of any duration caused enrichment in the α-helical content at the sacrifice of the unordered sequences in GOx while the presence of SWNTs, regardless of length and/or functionality, reduced the β-sheet content of pristine GOx. Surface profiling by white light interferometry revealed that ultrasonication produced some aggregation of GOx and that GOx effectively debundled the SWNT. Supramolecular conjugates formed from shorter, -OH functionalized SWNTs using longer sonication times (60 min) gave the most favored combination for forming bioactive conjugates.

The dual responsive Electrochemical Cell-on-a-Chip Microdisc Electrode Array (ECC MDEA 5037) is a recently developed electrochemical transducer for use in a wireless, implantable biosensor system for the continuous measurement of interstitial glucose and lactate. Fabrication of the biorecognition membrane via pyrrole electropolymerization and both in vitro and in vivo characterization of the resulting biotransducer is described. The influence of EDC-NHS covalent conjugation of glucose oxidase with 4-(3-pyrrolyl) butyric acid (monomerization) and with 4-sulfobenzoic acid (sulfonization) on biosensor performance was examined. As the extent of enzyme conjugation was increased sensitivity decreased for monomerized enzymes but increased for sulfonized enzymes. Implanted biotransducers were examined in a Sprague-Dawley rat hemorrhage model. Resection after 4 h and subsequent in vitro re-characterization showed a decreased sensitivity from 0.68 (±0.40) to 0.22 (±0.17) µA·cm⁻²·mM⁻¹, an increase in the limit of detection from 0.05 (±0.03) to 0.27 (±0.27) mM and a six-fold increase in the response time from 41 (±18) to 244 (±193) s. This evidence reconfirms the importance of biofouling at the bio-abio interface and the need for mitigation strategies to address the foreign body response.