The room-temperature (300 K), pulsed mode operation of a GaAs-based quantum-cascade laser is presented. This has been achieved by the use of a GaAs/Al0.45Ga0.55As heterostructure which offers the maximum Γ–Γ band offset (390 meV) for this material system without inducing the presence of indirect barrier states. Thus, better electron confinement is achieved, countering the loss of injection efficiency with temperature. These devices show ∼100 K increase in operating temperature with respect to equivalent designs using an GaAs/Al0.33Ga0.67As heterostructure. We also measure 600 mW peak power at 233 K a temperature readily accessible by Peltier coolers.

A photovoltaic intersubband detector based on electron transfer on a cascade of quantum levels is presented: A quantum cascade detector (QCD). The highest photoresponse of intersubband transition-based photovoltaic detectors is demonstrated: 35mA∕W at null bias. The deduced absorption is of the same order of magnitude as that of a classical quantum-well infrared photodetector, i.e., 20%. Because they work with no dark current, QCDs are very promising for small-pixel large focal plane array applications.

We demonstrate that, even in unstrained GaN quantum wells with AlGaN barriers, there exist giant electric fields as high as 1.5 MV/cm. These fields, resulting from the interplay of the piezoelectric and spontaneous polarizations in the well and barrier layers due to Fermi level alignment, induce large redshifts of the photoluminescence energy position and dramatically increase the carrier lifetime as the quantum well thickness increases.

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Background Detecting cancer at early stages significantly increases patient survival rates. Because lethal solid tumors often produce few symptoms before progressing to advanced, metastatic disease, diagnosis frequently occurs when surgical resection is no longer curative. One promising approach to detect early-stage, curable cancers uses biomarkers present in circulating extracellular vesicles (EVs). To explore the feasibility of this approach, we developed an EV-based blood biomarker classifier from EV protein profiles to detect stages I and II pancreatic, ovarian, and bladder cancer. Methods Utilizing an alternating current electrokinetics (ACE) platform to purify EVs from plasma, we use multi-marker EV-protein measurements to develop a machine learning algorithm that can discriminate cancer cases from controls. The ACE isolation method requires small sample volumes, and the streamlined process permits integration into high-throughput workflows. Results In this case-control pilot study, comparison of 139 pathologically confirmed stage I and II cancer cases representing pancreatic, ovarian, or bladder patients against 184 control subjects yields an area under the curve (AUC) of 0.95 (95% CI: 0.92 to 0.97), with sensitivity of 71.2% (95% CI: 63.2 to 78.1) at 99.5% (97.0 to 99.9) specificity. Sensitivity is similar at both early stages [stage I: 70.5% (60.2 to 79.0) and stage II: 72.5% (59.1 to 82.9)]. Detection of stage I cancer reaches 95.5% in pancreatic, 74.4% in ovarian (73.1% in Stage IA) and 43.8% in bladder cancer. Conclusions This work demonstrates that an EV-based, multi-cancer test has potential clinical value for early cancer detection and warrants future expanded studies involving prospective cohorts with multi-year follow-up.