A review of our recent progress in complementary metal oxide semiconductor (CMOS) biological and chemical optical sensors using nanostructured materials will be presented. Specific examples will include a xerogel based glucose sensor and a photonic bandgap based chemical vapor sensor. Firstly, an optical glucose sensor based on nanoporous xerogels produced by sol-gel processing techniques for encapsulating the enzyme, glucose oxidase (GOx), and an O2 sensitive luminophore will be discussed. The O2 sensors are based on the luminescence quenching of (tris(4,7-diphenyl-1,10-phenathroline) ruthenium(II) Ru(dpp)3 O2+. Luminophore quenching by O2 is governed by many factors, and in the simplest scenario, it is described by the linear SternVolmer equation . Glucose sensing is accomplished indirectly by monitoring the luminophore quenching by O2. Specifically, GOx consumes O2 as it oxidizes D-glucose to gluconic acid. This process is described in . Assuming that the dissolved O2 in the solution is always maintained at a constant level, the localized change in the dissolved O2 concentration can be translated to the glucose concentration. In order to improve the sensitivity of this technique, phase fluorometry is used. Since the excited state lifetime of the luminophore is directly related to the O2 concentration, and the lifetime is related to the phase difference between the excitation signal and the emitted signal, The O 2 concentration in a sample is determined by measuring this phase shift. Details of the fabrication process for the xerogel and recognition elements as well as the experimental setup and preparation are described in reference . Fig. 1 shows the basic structure of the xerogel based oxygen sensor. The resulting response curves for glucose sensing are shown in Fig. 2. Secondly, porous photonic bandgap (PPBG) structures using photosensitive polymers will be demonstrated as vapor sensors. These PPBG structures are fabricated by holographic interferometry (HI) as shown in Fig. 3. In HI, laser beams interfere and produce periodic patterns dependent on the amplitude, phase, polarization, wavelength and interference angle of the coincident beams. This technique allows one to readily create transmission and/or reflection gratings. For a reflection grating, the total internal reflection (TIR) between the photopolymer and glass, using one beam, forms an index modulation pattern in the polymer with interference fringes parallel to the film. For a transmission grating, two beams form an interference pattern that yields index modulation in the photopolymer with fringes perpendicular to the film. In both cases, the spacing of the grating is determined by the incident angle Θr or Θt as indicated in Fig. 3. The pre-polymer syrup, used in the experiments, and experimental setup can be found in reference . An SEM picture of a typical fabricated PPBG film is shown in Fig. 4. The PPBG structure provides a tunable optical spectrum while providing a high surface area porous material for chemical and biochemical sensing. Example applications of these PPBG structures for vapor sensing (low specificity) and for oxygen sensing (high specificity) will be presented. The O2 concentration as determined from the quenching of (tris(4,7-diphenyl-1,10-phenathroline)ruthenium(II) [Ru(dpp)3]2+ in the PPBG structure is shown Fig. 5. As should be expected, the emission is quenched as the oxygen concentration is increased and this sensor shows good dynamic range from O to 100 % oxygen concentration. By using 2-hydroxyethylmethacrylate (HEMA) as a hydrophilic component and by modifying the existing fabrication recipe for the conventional grating, we have succeeded in fabricating semi hydrophilic PPBG grating structures for biocompatible applications. Finally, the use of these sensor elements, along with a number of other elements that we have developed, within diversified arrays will be discussed. In particular, we will discuss the use of an artificial neural network (ANN) system that consists of two sequential neural networks: a multilayer perceptron network for feature extraction and the Kohonen self-organizing map for pattern classification. The combination of the sensor array with the ANN yields information for unknown samples that is significantly more accurate over the full concentration range as well as a greatly improved level of precision when compared to any single sensor element .