Exponential Growth / Decay Calculator (N = N₀·e^(kt))
The exponential model N(t) = N₀ · e^(k·t) is the canonical form for continuous change — bacteria, populations, radioactive isotopes, continuously compounded interest, Newton cooling and first-order pharmacokinetics all follow it. Enter N₀, k and t to immediately see N(t), the multiplier e^(k·t), the percent change per unit time, and the doubling time (when k > 0) or half-life (when k < 0).
Check inputs: N₀ ≥ 0, k and t finite real numbers.
Value at t — N(t)
164.87
N(t) = N₀ · e^(k·t)
Multiplier e^(k·t)
1.6487
Change per unit time
+5.13 %
Doubling time t_d
13.86
(units = unit of t)
Formula: N(t) = N₀ · e^(k·t). Models continuous compounding, populations, radioactive decay, Newton cooling, first-order pharmacokinetics and more.
Formula
N(t) = N₀ · e^(k · t) Doubling time (k > 0): t_d = ln 2 ⁄ k Half-life (k < 0): t½ = ln 2 ⁄ |k| Change per unit time: (e^k − 1) × 100 % Solve for time: t = ln(N ⁄ N₀) ⁄ k
- · k is the *continuous* rate, not the discrete annual compound rate; 100·e^0.05 ≈ 105.13 versus 100·1.05 = 105 (~0.13 % apart). To convert a discrete annual rate r to continuous k: k = ln(1 + r).
- · The units of k and t must be reciprocals: k = 0.05/yr requires t in years; k = 0.05/month requires t in months.
- · Useful anchors: global human population long-run r ≈ 1.1 %/yr → k ≈ 0.011 (doubles in ~63 yr); C-14 half-life 5,730 yr → k ≈ −1.21 × 10⁻⁴/yr; Tc-99m half-life 6.01 h → k ≈ −0.115/h; a drug with a 4 h half-life has a first-order elimination constant ≈ 0.173/h.
- · The exponential model captures the "unbounded-resources" early phase; once a carrying capacity bites, switch to the logistic model dN/dt = k·N·(1 − N/K).
- · Watch the sign of k: radioactive decay, drug elimination and Newton cooling all use a negative k. Cooling is sometimes written (T − T_env) = (T₀ − T_env)·e^(−kt) with the minus sign absorbed into the formula.
- · References: Strogatz, "Nonlinear Dynamics and Chaos", §2.0; Boyce & DiPrima, "Elementary Differential Equations", Ch. 1.1; Sauer, "Numerical Analysis", §1.1.
Frequently asked
What is the difference between the continuous rate k and the annual rate r — why does e^k ≠ 1 + r?
A "5 % annual rate" can mean two different things. Discrete: each 31 Dec the total is 5 % higher than last year — N(t) = N₀·(1 + 0.05)ᵗ. Continuous: at every instant the quantity grows at 5 %/yr — N(t) = N₀·e^(0.05·t). The continuous form is slightly larger because the "interest on interest" compounds infinitely often: 100·e^0.05 ≈ 105.127 versus 100·1.05 = 105, about 0.127 % higher. To convert: r_discrete = e^k − 1, equivalently k = ln(1 + r). Biology, physics and radioactivity typically quote k; finance typically quotes r. If your bank statement says APY 5 % and you want to use this calculator, set k = ln(1.05) ≈ 0.04879.
How do I back out k from a known half-life (or vice versa)?
They are reciprocals: t½ = ln 2 / |k|, so |k| = ln 2 / t½ ≈ 0.6931 / t½. Half-life is always positive, while the decay k is negative — so k = −ln 2 / t½. Examples: (1) Tc-99m, a common nuclear-medicine tracer, has a 6.01 h half-life → k ≈ −0.6931 / 6.01 ≈ −0.1153 /h; ~50 % remains at 6 h, ~25 % at 12 h. (2) Paracetamol plasma half-life ≈ 2 h → k ≈ −0.347 /h; about a quarter remains at 4 h, matching the "every 4–6 h dose" rule of thumb. Doubling time works the same way: t_d = ln 2 / k, so k = ln 2 / t_d. The familiar "Rule of 72" is the discrete-compounding approximation (a quantity growing at r % per year doubles in ≈ 72 / r years) — derived from ln 2 ≈ 0.6931 and a small-r Taylor expansion.
When do I need a logistic (S-curve) model instead of pure exponential growth?
Pure exponential growth assumes "unbounded resources" — dN/dt = k·N, with every individual free to divide and consume resources. In reality almost every system eventually hits a carrying capacity K (food, space, market saturation), and the curve flattens into an S shape. The logistic model dN/dt = k·N·(1 − N/K), proposed by Verhulst in 1838, adds the saturation term; in the early phase (N ≪ K) it reduces to pure exponential, and as N → K it plateaus at K. Practical signals: (1) bacterial cultures in rich media grow exponentially for the first few hours, then hit K and turn logistic by 12–24 h; (2) technology / virus adoption curves (COVID case counts, TikTok users) look exponential at first but show an inflection point later; (3) Moore's Law looked exponential for decades, but transistor scaling has now hit atomic limits — the model needs updating. Rule of thumb: once N/K exceeds about 10 %, pure exponential meaningfully over-predicts — switch to logistic or fit the empirical data directly.
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