Science lessons & courses

From thermodynamics through grid engineering — the physics, the units, and the engineering trade-offs of every major energy source. Six lessons cover energy and power units, combustion and thermal cycles, nuclear fission, nuclear fusion, solar and wind and batteries, and the grid that ties them together. Mechanism-first, evergreen, no predictions.

How DNA encodes proteins, how CRISPR/Cas9 cuts and repairs DNA, how base and prime editors achieve precision without breaks, what GLP-1 agonists do across multiple tissues, how clinical trial evidence is built, and how to read the resulting medical evidence. Six lessons, mechanism-first, evidence-aware.

What a qubit actually is mathematically, how entanglement and circuits compose, the hardware approaches and their trade-offs, why error correction is the central engineering problem, where algorithmic quantum speedup lives, and how cryptography adapts. Six lessons, math-load-bearing, with no predictions about timelines.

A six-lesson path from the physics of silicon to the global chip industry. Start with bands, doping, and the PN junction; build up through diodes and transistors to CMOS gates; then walk the wafer through a fab, decode '5 nm' marketing, and trace how Dennard scaling died and chiplets carry Moore's law forward. By the end you can read a chip datasheet, follow a TSMC roadmap, and explain why every modern SoC is half-dark.

Why an electrical grid is one giant synchronous machine, what frequency stability and rotational inertia actually mean, how dispatch ordering and ancillary services keep the lights on, and why variable renewable integration is fundamentally an engineering problem at the system level.

Why photovoltaics have a hard thermodynamic ceiling (Shockley-Queisser), why wind power scales with the cube of velocity (Betz), how lithium-ion chemistries actually differ, the learning-curve mathematics that produced the cost declines, and where the materials supply chains concentrate.

What it takes to fuse hydrogen isotopes — the four conditions (temperature, density, confinement time, energy gain), the three main approaches (magnetic, inertial, magnetized target), the engineering problems (tritium, neutrons, materials) that remain after the physics is in hand.

How fission releases energy from heavy nuclei, why neutron moderation determines reactor design, the structural choices behind LWR, CANDU, gas-cooled, fast, and molten-salt reactors, what the fuel cycle actually consists of, and where waste, cost, and safety arguments sit.

How hydrocarbon combustion releases energy as heat, the four canonical thermal cycles that convert that heat into mechanical or electrical work (Rankine, Brayton, Otto, Diesel), why combined-cycle gas plants reach 60% efficiency, and how carbon intensity scales with cycle and fuel choice.

The handful of quantitative concepts that make every energy debate readable — joules and watts, energy density, capacity factor, levelized cost of energy, exergy — and what each one is good and bad at communicating.

How to read a clinical trial result with discipline — the difference between absolute and relative risk reduction, what number-needed-to-treat captures, what confidence intervals actually mean, the hierarchy of evidence quality, and why statistical significance is not the same as clinical importance.

The phased structure of drug development from preclinical work through phase IV surveillance, what each phase actually establishes, the 90% attrition rate and where it lives, the difference between surrogate and hard clinical endpoints, and what regulatory approval pathways guarantee.

What the GLP-1 hormone does in normal physiology, why agonists of its receptor produce effects on glucose, gastric emptying, satiety, and weight, how peptide engineering achieves week-long duration, and what the clinical trial evidence shows beyond glycemic control.

How base editors convert one base to another by chemistry rather than by cutting, how prime editors use a programmable template and reverse transcription to make arbitrary small edits, and the structural trade-offs between scope, efficiency, and off-target activity.

How CRISPR/Cas9 cuts a specific DNA sequence using a programmable guide RNA, the two cellular repair pathways that determine whether the edit is a disruption or a correction, the structural problem of off-target effects, and what delivery into human cells actually requires.

The flow of information from DNA through mRNA to protein, what mutations actually change, why single-base changes can have outsized consequences, and the structural reason genome editing aims at DNA specifically.

What cryptographic schemes Shor's algorithm threatens, what post-quantum schemes replace them, the math behind lattice-based cryptography, the NIST standardization process and its outputs, and the operational mechanics of a real-world cryptographic migration.

Shor, Grover, Hamiltonian simulation, HHL — the catalog of known quantum-algorithmic speedups, what 'speedup' precisely means in each case, and the structural reasons most problems do not gain exponential advantage.

Why classical error correction does not directly transfer to qubits, how stabilizer codes and syndrome measurement work around the no-cloning constraint, the surface code as the leading approach, and the math of physical-to-logical qubit overhead.

Six families of physical qubit implementations and the engineering trade-offs that distinguish them — coherence time, gate time and fidelity, scalability, and control complexity. The numbers behind 'which is best' depend on which metric you care about.

What entanglement is mathematically, how Bell states are built from Hadamard and CNOT, how quantum circuits compose, and why measurement on entangled subsystems looks correlated regardless of separation.

The mathematical object behind a qubit — a complex unit vector in a two-dimensional Hilbert space — and why measurement collapses superposition. The structural difference between a quantum state and a classical bit, expressed in math.

Why Dennard scaling died in 2005, how the industry kept Moore's law alive through FinFET, gate-all-around, and chiplet packaging, and why modern chips look like zoos of specialized accelerators sitting half-dark. The constraints that produced the modern SoC and the frontiers that come next.

How a near-perfect silicon ingot becomes a billion-transistor chip. The 600-step fab cycle, photolithography down to 13.5 nm EUV (vaporized tin droplets at 220,000 °C), ion implantation for doping, the truth behind '5 nm' node naming, and the chokepoint-heavy global supply chain.

How pairing NMOS with PMOS creates digital gates that draw zero steady current. The CMOS inverter, NAND/NOR construction, the static-vs-dynamic power split that DVFS exploits, propagation delay and fan-out, and the scaling path from one inverter to a 25-billion-transistor SoC.

The two transistor families that built modern electronics. BJTs as current-controlled amplifiers, MOSFETs as voltage-controlled switches with a capacitor for a gate, the three operating regions of each, and the four reasons MOSFETs ended up running every digital chip.

From a single PN junction to the four-diode bridge in every wall adapter. The IV curve, rectification, Zener voltage references, fast Schottky diodes for switching supplies, LEDs and photodiodes turning current to light and back, and the five failure modes that bite real designs.

What makes silicon work where diamond won't, why doping turns an insulator into a tunable conductor, and how slapping p-type silicon next to n-type creates the depletion region that becomes a diode. The physics every chip is built on.